GAM
2022-09-01
knitr::opts_chunk$set(echo = TRUE)1. Carga de Librerias
# libreria de lectura de bitmaps para bmp, jpeg, png y tiff
library(readbitmap)## Warning: package 'readbitmap' was built under R version 4.1.2library(fda)## Warning: package 'fda' was built under R version 4.1.3## Loading required package: splines## Loading required package: fds## Warning: package 'fds' was built under R version 4.1.3## Loading required package: rainbow## Warning: package 'rainbow' was built under R version 4.1.3## Loading required package: MASS## Warning: package 'MASS' was built under R version 4.1.3## Loading required package: pcaPP## Warning: package 'pcaPP' was built under R version 4.1.3## Loading required package: RCurl## Warning: package 'RCurl' was built under R version 4.1.3## Loading required package: deSolve## Warning: package 'deSolve' was built under R version 4.1.3##
## Attaching package: 'fda'## The following object is masked from 'package:graphics':
##
## matplotlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.3##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
##
## select## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(caret)## Warning: package 'caret' was built under R version 4.1.3## Loading required package: lattice## Warning: package 'lattice' was built under R version 4.1.3##
## Attaching package: 'lattice'## The following object is masked from 'package:fda':
##
## melanomalibrary(fda.usc)## Warning: package 'fda.usc' was built under R version 4.1.3## Loading required package: mgcv## Warning: package 'mgcv' was built under R version 4.1.3## Loading required package: nlme## Warning: package 'nlme' was built under R version 4.1.3##
## Attaching package: 'nlme'## The following object is masked from 'package:dplyr':
##
## collapse## This is mgcv 1.8-41. For overview type 'help("mgcv-package")'.## fda.usc is running sequentially usign foreach package
## Please, execute ops.fda.usc() once to run in local parallel mode
## Deprecated functions: min.basis, min.np, anova.hetero, anova.onefactor, anova.RPm
## New functions: optim.basis, optim.np, fanova.hetero, fanova.onefactor, fanova.RPm
## ----------------------------------------------------------------------------------A. Sin centrado de los datos
2. Cargar los datos funcionales de las imagenes de entrenamiento y crear objeto fd con dichas curvas en base bspline
imagenesEntxey <- NULL
for (j in 1:7) {
imagenductil <- read.bitmap(paste("DuctilEnt", j,".jpg", sep = "")) # crea una matriz de x filas y columanas y z=3 canales
imagenductil <- imagenductil [,,1] # Deja un solo canal
imagenductilt <- t(imagenductil)
imagenductilxey <- rbind(imagenductil,imagenductilt)
imagenductilxey <- t(imagenductilxey)
imagenesEntxey <- cbind(imagenesEntxey,imagenductilxey)}
attributes(imagenesEntxey)## $dim
## [1] 200 2800for (j in 1:7) {
imagenfragil <- read.bitmap(paste("FragilEnt", j,".jpg", sep = "")) # crea una matriz de x filas y columanas y z=3 canales
imagenfragil <- imagenfragil [,,1] # Deja un solo canal
imagenfragilt <- t(imagenfragil)
imagenfragilxey <- rbind(imagenfragil,imagenfragilt)
imagenfragilxey <- t(imagenfragilxey)
imagenesEntxey <- cbind(imagenesEntxey,imagenfragilxey)}
attributes(imagenesEntxey)## $dim
## [1] 200 5600for (j in 1:7) {
imagenfatiga <- read.bitmap(paste("FatigaEnt", j,".jpg", sep = "")) # crea una matriz de x filas y columanas y z=3 canales
imagenfatiga <- imagenfatiga [,,1] # Deja un solo canal
imagenfatigat <- t(imagenfatiga)
imagenfatigaxey <- rbind(imagenfatiga,imagenfatigat)
imagenfatigaxey <- t(imagenfatigaxey)
imagenesEntxey <- cbind(imagenesEntxey,imagenfatigaxey)}
attributes(imagenesEntxey)## $dim
## [1] 200 8400# Convertir en Data Frame Entrenamiento
imagenesEntxeydf <- as.data.frame (imagenesEntxey)
attributes(imagenesEntxeydf)## $names
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9"
## [10] "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18"
## [19] "V19" "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27"
## [28] "V28" "V29" "V30" "V31" "V32" "V33" "V34" "V35" "V36"
## [37] "V37" "V38" "V39" "V40" "V41" "V42" "V43" "V44" "V45"
## [46] "V46" "V47" "V48" "V49" "V50" "V51" "V52" "V53" "V54"
## [55] "V55" "V56" "V57" "V58" "V59" "V60" "V61" "V62" "V63"
## [64] "V64" "V65" "V66" "V67" "V68" "V69" "V70" "V71" "V72"
## [73] "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80" "V81"
## [82] "V82" "V83" "V84" "V85" "V86" "V87" "V88" "V89" "V90"
## [91] "V91" "V92" "V93" "V94" "V95" "V96" "V97" "V98" "V99"
## [100] "V100" "V101" "V102" "V103" "V104" "V105" "V106" "V107" "V108"
## [109] "V109" "V110" "V111" "V112" "V113" "V114" "V115" "V116" "V117"
## [118] "V118" "V119" "V120" "V121" "V122" "V123" "V124" "V125" "V126"
## [127] "V127" "V128" "V129" "V130" "V131" "V132" "V133" "V134" "V135"
## [136] "V136" "V137" "V138" "V139" "V140" "V141" "V142" "V143" "V144"
## [145] "V145" "V146" "V147" "V148" "V149" "V150" "V151" "V152" "V153"
## [154] "V154" "V155" "V156" "V157" "V158" "V159" "V160" "V161" "V162"
## [163] "V163" "V164" "V165" "V166" "V167" "V168" "V169" "V170" "V171"
## [172] "V172" "V173" "V174" "V175" "V176" "V177" "V178" "V179" "V180"
## [181] "V181" "V182" "V183" "V184" "V185" "V186" "V187" "V188" "V189"
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## [199] "V199" "V200" "V201" "V202" "V203" "V204" "V205" "V206" "V207"
## [208] "V208" "V209" "V210" "V211" "V212" "V213" "V214" "V215" "V216"
## [217] "V217" "V218" "V219" "V220" "V221" "V222" "V223" "V224" "V225"
## [226] "V226" "V227" "V228" "V229" "V230" "V231" "V232" "V233" "V234"
## [235] "V235" "V236" "V237" "V238" "V239" "V240" "V241" "V242" "V243"
## [244] "V244" "V245" "V246" "V247" "V248" "V249" "V250" "V251" "V252"
## [253] "V253" "V254" "V255" "V256" "V257" "V258" "V259" "V260" "V261"
## [262] "V262" "V263" "V264" "V265" "V266" "V267" "V268" "V269" "V270"
## [271] "V271" "V272" "V273" "V274" "V275" "V276" "V277" "V278" "V279"
## [280] "V280" "V281" "V282" "V283" "V284" "V285" "V286" "V287" "V288"
## [289] "V289" "V290" "V291" "V292" "V293" "V294" "V295" "V296" "V297"
## [298] "V298" "V299" "V300" "V301" "V302" "V303" "V304" "V305" "V306"
## [307] "V307" "V308" "V309" "V310" "V311" "V312" "V313" "V314" "V315"
## [316] "V316" "V317" "V318" "V319" "V320" "V321" "V322" "V323" "V324"
## [325] "V325" "V326" "V327" "V328" "V329" "V330" "V331" "V332" "V333"
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## [388] "V388" "V389" "V390" "V391" "V392" "V393" "V394" "V395" "V396"
## [397] "V397" "V398" "V399" "V400" "V401" "V402" "V403" "V404" "V405"
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## [415] "V415" "V416" "V417" "V418" "V419" "V420" "V421" "V422" "V423"
## [424] "V424" "V425" "V426" "V427" "V428" "V429" "V430" "V431" "V432"
## [433] "V433" "V434" "V435" "V436" "V437" "V438" "V439" "V440" "V441"
## [442] "V442" "V443" "V444" "V445" "V446" "V447" "V448" "V449" "V450"
## [451] "V451" "V452" "V453" "V454" "V455" "V456" "V457" "V458" "V459"
## [460] "V460" "V461" "V462" "V463" "V464" "V465" "V466" "V467" "V468"
## [469] "V469" "V470" "V471" "V472" "V473" "V474" "V475" "V476" "V477"
## [478] "V478" "V479" "V480" "V481" "V482" "V483" "V484" "V485" "V486"
## [487] "V487" "V488" "V489" "V490" "V491" "V492" "V493" "V494" "V495"
## [496] "V496" "V497" "V498" "V499" "V500" "V501" "V502" "V503" "V504"
## [505] "V505" "V506" "V507" "V508" "V509" "V510" "V511" "V512" "V513"
## [514] "V514" "V515" "V516" "V517" "V518" "V519" "V520" "V521" "V522"
## [523] "V523" "V524" "V525" "V526" "V527" "V528" "V529" "V530" "V531"
## [532] "V532" "V533" "V534" "V535" "V536" "V537" "V538" "V539" "V540"
## [541] "V541" "V542" "V543" "V544" "V545" "V546" "V547" "V548" "V549"
## [550] "V550" "V551" "V552" "V553" "V554" "V555" "V556" "V557" "V558"
## [559] "V559" "V560" "V561" "V562" "V563" "V564" "V565" "V566" "V567"
## [568] "V568" "V569" "V570" "V571" "V572" "V573" "V574" "V575" "V576"
## [577] "V577" "V578" "V579" "V580" "V581" "V582" "V583" "V584" "V585"
## [586] "V586" "V587" "V588" "V589" "V590" "V591" "V592" "V593" "V594"
## [595] "V595" "V596" "V597" "V598" "V599" "V600" "V601" "V602" "V603"
## [604] "V604" "V605" "V606" "V607" "V608" "V609" "V610" "V611" "V612"
## [613] "V613" "V614" "V615" "V616" "V617" "V618" "V619" "V620" "V621"
## [622] "V622" "V623" "V624" "V625" "V626" "V627" "V628" "V629" "V630"
## [631] "V631" "V632" "V633" "V634" "V635" "V636" "V637" "V638" "V639"
## [640] "V640" "V641" "V642" "V643" "V644" "V645" "V646" "V647" "V648"
## [649] "V649" "V650" "V651" "V652" "V653" "V654" "V655" "V656" "V657"
## [658] "V658" "V659" "V660" "V661" "V662" "V663" "V664" "V665" "V666"
## [667] "V667" "V668" "V669" "V670" "V671" "V672" "V673" "V674" "V675"
## [676] "V676" "V677" "V678" "V679" "V680" "V681" "V682" "V683" "V684"
## [685] "V685" "V686" "V687" "V688" "V689" "V690" "V691" "V692" "V693"
## [694] "V694" "V695" "V696" "V697" "V698" "V699" "V700" "V701" "V702"
## [703] "V703" "V704" "V705" "V706" "V707" "V708" "V709" "V710" "V711"
## [712] "V712" "V713" "V714" "V715" "V716" "V717" "V718" "V719" "V720"
## [721] "V721" "V722" "V723" "V724" "V725" "V726" "V727" "V728" "V729"
## [730] "V730" "V731" "V732" "V733" "V734" "V735" "V736" "V737" "V738"
## [739] "V739" "V740" "V741" "V742" "V743" "V744" "V745" "V746" "V747"
## [748] "V748" "V749" "V750" "V751" "V752" "V753" "V754" "V755" "V756"
## [757] "V757" "V758" "V759" "V760" "V761" "V762" "V763" "V764" "V765"
## [766] "V766" "V767" "V768" "V769" "V770" "V771" "V772" "V773" "V774"
## [775] "V775" "V776" "V777" "V778" "V779" "V780" "V781" "V782" "V783"
## [784] "V784" "V785" "V786" "V787" "V788" "V789" "V790" "V791" "V792"
## [793] "V793" "V794" "V795" "V796" "V797" "V798" "V799" "V800" "V801"
## [802] "V802" "V803" "V804" "V805" "V806" "V807" "V808" "V809" "V810"
## [811] "V811" "V812" "V813" "V814" "V815" "V816" "V817" "V818" "V819"
## [820] "V820" "V821" "V822" "V823" "V824" "V825" "V826" "V827" "V828"
## [829] "V829" "V830" "V831" "V832" "V833" "V834" "V835" "V836" "V837"
## [838] "V838" "V839" "V840" "V841" "V842" "V843" "V844" "V845" "V846"
## [847] "V847" "V848" "V849" "V850" "V851" "V852" "V853" "V854" "V855"
## [856] "V856" "V857" "V858" "V859" "V860" "V861" "V862" "V863" "V864"
## [865] "V865" "V866" "V867" "V868" "V869" "V870" "V871" "V872" "V873"
## [874] "V874" "V875" "V876" "V877" "V878" "V879" "V880" "V881" "V882"
## [883] "V883" "V884" "V885" "V886" "V887" "V888" "V889" "V890" "V891"
## [892] "V892" "V893" "V894" "V895" "V896" "V897" "V898" "V899" "V900"
## [901] "V901" "V902" "V903" "V904" "V905" "V906" "V907" "V908" "V909"
## [910] "V910" "V911" "V912" "V913" "V914" "V915" "V916" "V917" "V918"
## [919] "V919" "V920" "V921" "V922" "V923" "V924" "V925" "V926" "V927"
## [928] "V928" "V929" "V930" "V931" "V932" "V933" "V934" "V935" "V936"
## [937] "V937" "V938" "V939" "V940" "V941" "V942" "V943" "V944" "V945"
## [946] "V946" "V947" "V948" "V949" "V950" "V951" "V952" "V953" "V954"
## [955] "V955" "V956" "V957" "V958" "V959" "V960" "V961" "V962" "V963"
## [964] "V964" "V965" "V966" "V967" "V968" "V969" "V970" "V971" "V972"
## [973] "V973" "V974" "V975" "V976" "V977" "V978" "V979" "V980" "V981"
## [982] "V982" "V983" "V984" "V985" "V986" "V987" "V988" "V989" "V990"
## [991] "V991" "V992" "V993" "V994" "V995" "V996" "V997" "V998" "V999"
## [1000] "V1000" "V1001" "V1002" "V1003" "V1004" "V1005" "V1006" "V1007" "V1008"
## [1009] "V1009" "V1010" "V1011" "V1012" "V1013" "V1014" "V1015" "V1016" "V1017"
## [1018] "V1018" "V1019" "V1020" "V1021" "V1022" "V1023" "V1024" "V1025" "V1026"
## [1027] "V1027" "V1028" "V1029" "V1030" "V1031" "V1032" "V1033" "V1034" "V1035"
## [1036] "V1036" "V1037" "V1038" "V1039" "V1040" "V1041" "V1042" "V1043" "V1044"
## [1045] "V1045" "V1046" "V1047" "V1048" "V1049" "V1050" "V1051" "V1052" "V1053"
## [1054] "V1054" "V1055" "V1056" "V1057" "V1058" "V1059" "V1060" "V1061" "V1062"
## [1063] "V1063" "V1064" "V1065" "V1066" "V1067" "V1068" "V1069" "V1070" "V1071"
## [1072] "V1072" "V1073" "V1074" "V1075" "V1076" "V1077" "V1078" "V1079" "V1080"
## [1081] "V1081" "V1082" "V1083" "V1084" "V1085" "V1086" "V1087" "V1088" "V1089"
## [1090] "V1090" "V1091" "V1092" "V1093" "V1094" "V1095" "V1096" "V1097" "V1098"
## [1099] "V1099" "V1100" "V1101" "V1102" "V1103" "V1104" "V1105" "V1106" "V1107"
## [1108] "V1108" "V1109" "V1110" "V1111" "V1112" "V1113" "V1114" "V1115" "V1116"
## [1117] "V1117" "V1118" "V1119" "V1120" "V1121" "V1122" "V1123" "V1124" "V1125"
## [1126] "V1126" "V1127" "V1128" "V1129" "V1130" "V1131" "V1132" "V1133" "V1134"
## [1135] "V1135" "V1136" "V1137" "V1138" "V1139" "V1140" "V1141" "V1142" "V1143"
## [1144] "V1144" "V1145" "V1146" "V1147" "V1148" "V1149" "V1150" "V1151" "V1152"
## [1153] "V1153" "V1154" "V1155" "V1156" "V1157" "V1158" "V1159" "V1160" "V1161"
## [1162] "V1162" "V1163" "V1164" "V1165" "V1166" "V1167" "V1168" "V1169" "V1170"
## [1171] "V1171" "V1172" "V1173" "V1174" "V1175" "V1176" "V1177" "V1178" "V1179"
## [1180] "V1180" "V1181" "V1182" "V1183" "V1184" "V1185" "V1186" "V1187" "V1188"
## [1189] "V1189" "V1190" "V1191" "V1192" "V1193" "V1194" "V1195" "V1196" "V1197"
## [1198] "V1198" "V1199" "V1200" "V1201" "V1202" "V1203" "V1204" "V1205" "V1206"
## [1207] "V1207" "V1208" "V1209" "V1210" "V1211" "V1212" "V1213" "V1214" "V1215"
## [1216] "V1216" "V1217" "V1218" "V1219" "V1220" "V1221" "V1222" "V1223" "V1224"
## [1225] "V1225" "V1226" "V1227" "V1228" "V1229" "V1230" "V1231" "V1232" "V1233"
## [1234] "V1234" "V1235" "V1236" "V1237" "V1238" "V1239" "V1240" "V1241" "V1242"
## [1243] "V1243" "V1244" "V1245" "V1246" "V1247" "V1248" "V1249" "V1250" "V1251"
## [1252] "V1252" "V1253" "V1254" "V1255" "V1256" "V1257" "V1258" "V1259" "V1260"
## [1261] "V1261" "V1262" "V1263" "V1264" "V1265" "V1266" "V1267" "V1268" "V1269"
## [1270] "V1270" "V1271" "V1272" "V1273" "V1274" "V1275" "V1276" "V1277" "V1278"
## [1279] "V1279" "V1280" "V1281" "V1282" "V1283" "V1284" "V1285" "V1286" "V1287"
## [1288] "V1288" "V1289" "V1290" "V1291" "V1292" "V1293" "V1294" "V1295" "V1296"
## [1297] "V1297" "V1298" "V1299" "V1300" "V1301" "V1302" "V1303" "V1304" "V1305"
## [1306] "V1306" "V1307" "V1308" "V1309" "V1310" "V1311" "V1312" "V1313" "V1314"
## [1315] "V1315" "V1316" "V1317" "V1318" "V1319" "V1320" "V1321" "V1322" "V1323"
## [1324] "V1324" "V1325" "V1326" "V1327" "V1328" "V1329" "V1330" "V1331" "V1332"
## [1333] "V1333" "V1334" "V1335" "V1336" "V1337" "V1338" "V1339" "V1340" "V1341"
## [1342] "V1342" "V1343" "V1344" "V1345" "V1346" "V1347" "V1348" "V1349" "V1350"
## [1351] "V1351" "V1352" "V1353" "V1354" "V1355" "V1356" "V1357" "V1358" "V1359"
## [1360] "V1360" "V1361" "V1362" "V1363" "V1364" "V1365" "V1366" "V1367" "V1368"
## [1369] "V1369" "V1370" "V1371" "V1372" "V1373" "V1374" "V1375" "V1376" "V1377"
## [1378] "V1378" "V1379" "V1380" "V1381" "V1382" "V1383" "V1384" "V1385" "V1386"
## [1387] "V1387" "V1388" "V1389" "V1390" "V1391" "V1392" "V1393" "V1394" "V1395"
## [1396] "V1396" "V1397" "V1398" "V1399" "V1400" "V1401" "V1402" "V1403" "V1404"
## [1405] "V1405" "V1406" "V1407" "V1408" "V1409" "V1410" "V1411" "V1412" "V1413"
## [1414] "V1414" "V1415" "V1416" "V1417" "V1418" "V1419" "V1420" "V1421" "V1422"
## [1423] "V1423" "V1424" "V1425" "V1426" "V1427" "V1428" "V1429" "V1430" "V1431"
## [1432] "V1432" "V1433" "V1434" "V1435" "V1436" "V1437" "V1438" "V1439" "V1440"
## [1441] "V1441" "V1442" "V1443" "V1444" "V1445" "V1446" "V1447" "V1448" "V1449"
## [1450] "V1450" "V1451" "V1452" "V1453" "V1454" "V1455" "V1456" "V1457" "V1458"
## [1459] "V1459" "V1460" "V1461" "V1462" "V1463" "V1464" "V1465" "V1466" "V1467"
## [1468] "V1468" "V1469" "V1470" "V1471" "V1472" "V1473" "V1474" "V1475" "V1476"
## [1477] "V1477" "V1478" "V1479" "V1480" "V1481" "V1482" "V1483" "V1484" "V1485"
## [1486] "V1486" "V1487" "V1488" "V1489" "V1490" "V1491" "V1492" "V1493" "V1494"
## [1495] "V1495" "V1496" "V1497" "V1498" "V1499" "V1500" "V1501" "V1502" "V1503"
## [1504] "V1504" "V1505" "V1506" "V1507" "V1508" "V1509" "V1510" "V1511" "V1512"
## [1513] "V1513" "V1514" "V1515" "V1516" "V1517" "V1518" "V1519" "V1520" "V1521"
## [1522] "V1522" "V1523" "V1524" "V1525" "V1526" "V1527" "V1528" "V1529" "V1530"
## [1531] "V1531" "V1532" "V1533" "V1534" "V1535" "V1536" "V1537" "V1538" "V1539"
## [1540] "V1540" "V1541" "V1542" "V1543" "V1544" "V1545" "V1546" "V1547" "V1548"
## [1549] "V1549" "V1550" "V1551" "V1552" "V1553" "V1554" "V1555" "V1556" "V1557"
## [1558] "V1558" "V1559" "V1560" "V1561" "V1562" "V1563" "V1564" "V1565" "V1566"
## [1567] "V1567" "V1568" "V1569" "V1570" "V1571" "V1572" "V1573" "V1574" "V1575"
## [1576] "V1576" "V1577" "V1578" "V1579" "V1580" "V1581" "V1582" "V1583" "V1584"
## [1585] "V1585" "V1586" "V1587" "V1588" "V1589" "V1590" "V1591" "V1592" "V1593"
## [1594] "V1594" "V1595" "V1596" "V1597" "V1598" "V1599" "V1600" "V1601" "V1602"
## [1603] "V1603" "V1604" "V1605" "V1606" "V1607" "V1608" "V1609" "V1610" "V1611"
## [1612] "V1612" "V1613" "V1614" "V1615" "V1616" "V1617" "V1618" "V1619" "V1620"
## [1621] "V1621" "V1622" "V1623" "V1624" "V1625" "V1626" "V1627" "V1628" "V1629"
## [1630] "V1630" "V1631" "V1632" "V1633" "V1634" "V1635" "V1636" "V1637" "V1638"
## [1639] "V1639" "V1640" "V1641" "V1642" "V1643" "V1644" "V1645" "V1646" "V1647"
## [1648] "V1648" "V1649" "V1650" "V1651" "V1652" "V1653" "V1654" "V1655" "V1656"
## [1657] "V1657" "V1658" "V1659" "V1660" "V1661" "V1662" "V1663" "V1664" "V1665"
## [1666] "V1666" "V1667" "V1668" "V1669" "V1670" "V1671" "V1672" "V1673" "V1674"
## [1675] "V1675" "V1676" "V1677" "V1678" "V1679" "V1680" "V1681" "V1682" "V1683"
## [1684] "V1684" "V1685" "V1686" "V1687" "V1688" "V1689" "V1690" "V1691" "V1692"
## [1693] "V1693" "V1694" "V1695" "V1696" "V1697" "V1698" "V1699" "V1700" "V1701"
## [1702] "V1702" "V1703" "V1704" "V1705" "V1706" "V1707" "V1708" "V1709" "V1710"
## [1711] "V1711" "V1712" "V1713" "V1714" "V1715" "V1716" "V1717" "V1718" "V1719"
## [1720] "V1720" "V1721" "V1722" "V1723" "V1724" "V1725" "V1726" "V1727" "V1728"
## [1729] "V1729" "V1730" "V1731" "V1732" "V1733" "V1734" "V1735" "V1736" "V1737"
## [1738] "V1738" "V1739" "V1740" "V1741" "V1742" "V1743" "V1744" "V1745" "V1746"
## [1747] "V1747" "V1748" "V1749" "V1750" "V1751" "V1752" "V1753" "V1754" "V1755"
## [1756] "V1756" "V1757" "V1758" "V1759" "V1760" "V1761" "V1762" "V1763" "V1764"
## [1765] "V1765" "V1766" "V1767" "V1768" "V1769" "V1770" "V1771" "V1772" "V1773"
## [1774] "V1774" "V1775" "V1776" "V1777" "V1778" "V1779" "V1780" "V1781" "V1782"
## [1783] "V1783" "V1784" "V1785" "V1786" "V1787" "V1788" "V1789" "V1790" "V1791"
## [1792] "V1792" "V1793" "V1794" "V1795" "V1796" "V1797" "V1798" "V1799" "V1800"
## [1801] "V1801" "V1802" "V1803" "V1804" "V1805" "V1806" "V1807" "V1808" "V1809"
## [1810] "V1810" "V1811" "V1812" "V1813" "V1814" "V1815" "V1816" "V1817" "V1818"
## [1819] "V1819" "V1820" "V1821" "V1822" "V1823" "V1824" "V1825" "V1826" "V1827"
## [1828] "V1828" "V1829" "V1830" "V1831" "V1832" "V1833" "V1834" "V1835" "V1836"
## [1837] "V1837" "V1838" "V1839" "V1840" "V1841" "V1842" "V1843" "V1844" "V1845"
## [1846] "V1846" "V1847" "V1848" "V1849" "V1850" "V1851" "V1852" "V1853" "V1854"
## [1855] "V1855" "V1856" "V1857" "V1858" "V1859" "V1860" "V1861" "V1862" "V1863"
## [1864] "V1864" "V1865" "V1866" "V1867" "V1868" "V1869" "V1870" "V1871" "V1872"
## [1873] "V1873" "V1874" "V1875" "V1876" "V1877" "V1878" "V1879" "V1880" "V1881"
## [1882] "V1882" "V1883" "V1884" "V1885" "V1886" "V1887" "V1888" "V1889" "V1890"
## [1891] "V1891" "V1892" "V1893" "V1894" "V1895" "V1896" "V1897" "V1898" "V1899"
## [1900] "V1900" "V1901" "V1902" "V1903" "V1904" "V1905" "V1906" "V1907" "V1908"
## [1909] "V1909" "V1910" "V1911" "V1912" "V1913" "V1914" "V1915" "V1916" "V1917"
## [1918] "V1918" "V1919" "V1920" "V1921" "V1922" "V1923" "V1924" "V1925" "V1926"
## [1927] "V1927" "V1928" "V1929" "V1930" "V1931" "V1932" "V1933" "V1934" "V1935"
## [1936] "V1936" "V1937" "V1938" "V1939" "V1940" "V1941" "V1942" "V1943" "V1944"
## [1945] "V1945" "V1946" "V1947" "V1948" "V1949" "V1950" "V1951" "V1952" "V1953"
## [1954] "V1954" "V1955" "V1956" "V1957" "V1958" "V1959" "V1960" "V1961" "V1962"
## [1963] "V1963" "V1964" "V1965" "V1966" "V1967" "V1968" "V1969" "V1970" "V1971"
## [1972] "V1972" "V1973" "V1974" "V1975" "V1976" "V1977" "V1978" "V1979" "V1980"
## [1981] "V1981" "V1982" "V1983" "V1984" "V1985" "V1986" "V1987" "V1988" "V1989"
## [1990] "V1990" "V1991" "V1992" "V1993" "V1994" "V1995" "V1996" "V1997" "V1998"
## [1999] "V1999" "V2000" "V2001" "V2002" "V2003" "V2004" "V2005" "V2006" "V2007"
## [2008] "V2008" "V2009" "V2010" "V2011" "V2012" "V2013" "V2014" "V2015" "V2016"
## [2017] "V2017" "V2018" "V2019" "V2020" "V2021" "V2022" "V2023" "V2024" "V2025"
## [2026] "V2026" "V2027" "V2028" "V2029" "V2030" "V2031" "V2032" "V2033" "V2034"
## [2035] "V2035" "V2036" "V2037" "V2038" "V2039" "V2040" "V2041" "V2042" "V2043"
## [2044] "V2044" "V2045" "V2046" "V2047" "V2048" "V2049" "V2050" "V2051" "V2052"
## [2053] "V2053" "V2054" "V2055" "V2056" "V2057" "V2058" "V2059" "V2060" "V2061"
## [2062] "V2062" "V2063" "V2064" "V2065" "V2066" "V2067" "V2068" "V2069" "V2070"
## [2071] "V2071" "V2072" "V2073" "V2074" "V2075" "V2076" "V2077" "V2078" "V2079"
## [2080] "V2080" "V2081" "V2082" "V2083" "V2084" "V2085" "V2086" "V2087" "V2088"
## [2089] "V2089" "V2090" "V2091" "V2092" "V2093" "V2094" "V2095" "V2096" "V2097"
## [2098] "V2098" "V2099" "V2100" "V2101" "V2102" "V2103" "V2104" "V2105" "V2106"
## [2107] "V2107" "V2108" "V2109" "V2110" "V2111" "V2112" "V2113" "V2114" "V2115"
## [2116] "V2116" "V2117" "V2118" "V2119" "V2120" "V2121" "V2122" "V2123" "V2124"
## [2125] "V2125" "V2126" "V2127" "V2128" "V2129" "V2130" "V2131" "V2132" "V2133"
## [2134] "V2134" "V2135" "V2136" "V2137" "V2138" "V2139" "V2140" "V2141" "V2142"
## [2143] "V2143" "V2144" "V2145" "V2146" "V2147" "V2148" "V2149" "V2150" "V2151"
## [2152] "V2152" "V2153" "V2154" "V2155" "V2156" "V2157" "V2158" "V2159" "V2160"
## [2161] "V2161" "V2162" "V2163" "V2164" "V2165" "V2166" "V2167" "V2168" "V2169"
## [2170] "V2170" "V2171" "V2172" "V2173" "V2174" "V2175" "V2176" "V2177" "V2178"
## [2179] "V2179" "V2180" "V2181" "V2182" "V2183" "V2184" "V2185" "V2186" "V2187"
## [2188] "V2188" "V2189" "V2190" "V2191" "V2192" "V2193" "V2194" "V2195" "V2196"
## [2197] "V2197" "V2198" "V2199" "V2200" "V2201" "V2202" "V2203" "V2204" "V2205"
## [2206] "V2206" "V2207" "V2208" "V2209" "V2210" "V2211" "V2212" "V2213" "V2214"
## [2215] "V2215" "V2216" "V2217" "V2218" "V2219" "V2220" "V2221" "V2222" "V2223"
## [2224] "V2224" "V2225" "V2226" "V2227" "V2228" "V2229" "V2230" "V2231" "V2232"
## [2233] "V2233" "V2234" "V2235" "V2236" "V2237" "V2238" "V2239" "V2240" "V2241"
## [2242] "V2242" "V2243" "V2244" "V2245" "V2246" "V2247" "V2248" "V2249" "V2250"
## [2251] "V2251" "V2252" "V2253" "V2254" "V2255" "V2256" "V2257" "V2258" "V2259"
## [2260] "V2260" "V2261" "V2262" "V2263" "V2264" "V2265" "V2266" "V2267" "V2268"
## [2269] "V2269" "V2270" "V2271" "V2272" "V2273" "V2274" "V2275" "V2276" "V2277"
## [2278] "V2278" "V2279" "V2280" "V2281" "V2282" "V2283" "V2284" "V2285" "V2286"
## [2287] "V2287" "V2288" "V2289" "V2290" "V2291" "V2292" "V2293" "V2294" "V2295"
## [2296] "V2296" "V2297" "V2298" "V2299" "V2300" "V2301" "V2302" "V2303" "V2304"
## [2305] "V2305" "V2306" "V2307" "V2308" "V2309" "V2310" "V2311" "V2312" "V2313"
## [2314] "V2314" "V2315" "V2316" "V2317" "V2318" "V2319" "V2320" "V2321" "V2322"
## [2323] "V2323" "V2324" "V2325" "V2326" "V2327" "V2328" "V2329" "V2330" "V2331"
## [2332] "V2332" "V2333" "V2334" "V2335" "V2336" "V2337" "V2338" "V2339" "V2340"
## [2341] "V2341" "V2342" "V2343" "V2344" "V2345" "V2346" "V2347" "V2348" "V2349"
## [2350] "V2350" "V2351" "V2352" "V2353" "V2354" "V2355" "V2356" "V2357" "V2358"
## [2359] "V2359" "V2360" "V2361" "V2362" "V2363" "V2364" "V2365" "V2366" "V2367"
## [2368] "V2368" "V2369" "V2370" "V2371" "V2372" "V2373" "V2374" "V2375" "V2376"
## [2377] "V2377" "V2378" "V2379" "V2380" "V2381" "V2382" "V2383" "V2384" "V2385"
## [2386] "V2386" "V2387" "V2388" "V2389" "V2390" "V2391" "V2392" "V2393" "V2394"
## [2395] "V2395" "V2396" "V2397" "V2398" "V2399" "V2400" "V2401" "V2402" "V2403"
## [2404] "V2404" "V2405" "V2406" "V2407" "V2408" "V2409" "V2410" "V2411" "V2412"
## [2413] "V2413" "V2414" "V2415" "V2416" "V2417" "V2418" "V2419" "V2420" "V2421"
## [2422] "V2422" "V2423" "V2424" "V2425" "V2426" "V2427" "V2428" "V2429" "V2430"
## [2431] "V2431" "V2432" "V2433" "V2434" "V2435" "V2436" "V2437" "V2438" "V2439"
## [2440] "V2440" "V2441" "V2442" "V2443" "V2444" "V2445" "V2446" "V2447" "V2448"
## [2449] "V2449" "V2450" "V2451" "V2452" "V2453" "V2454" "V2455" "V2456" "V2457"
## [2458] "V2458" "V2459" "V2460" "V2461" "V2462" "V2463" "V2464" "V2465" "V2466"
## [2467] "V2467" "V2468" "V2469" "V2470" "V2471" "V2472" "V2473" "V2474" "V2475"
## [2476] "V2476" "V2477" "V2478" "V2479" "V2480" "V2481" "V2482" "V2483" "V2484"
## [2485] "V2485" "V2486" "V2487" "V2488" "V2489" "V2490" "V2491" "V2492" "V2493"
## [2494] "V2494" "V2495" "V2496" "V2497" "V2498" "V2499" "V2500" "V2501" "V2502"
## [2503] "V2503" "V2504" "V2505" "V2506" "V2507" "V2508" "V2509" "V2510" "V2511"
## [2512] "V2512" "V2513" "V2514" "V2515" "V2516" "V2517" "V2518" "V2519" "V2520"
## [2521] "V2521" "V2522" "V2523" "V2524" "V2525" "V2526" "V2527" "V2528" "V2529"
## [2530] "V2530" "V2531" "V2532" "V2533" "V2534" "V2535" "V2536" "V2537" "V2538"
## [2539] "V2539" "V2540" "V2541" "V2542" "V2543" "V2544" "V2545" "V2546" "V2547"
## [2548] "V2548" "V2549" "V2550" "V2551" "V2552" "V2553" "V2554" "V2555" "V2556"
## [2557] "V2557" "V2558" "V2559" "V2560" "V2561" "V2562" "V2563" "V2564" "V2565"
## [2566] "V2566" "V2567" "V2568" "V2569" "V2570" "V2571" "V2572" "V2573" "V2574"
## [2575] "V2575" "V2576" "V2577" "V2578" "V2579" "V2580" "V2581" "V2582" "V2583"
## [2584] "V2584" "V2585" "V2586" "V2587" "V2588" "V2589" "V2590" "V2591" "V2592"
## [2593] "V2593" "V2594" "V2595" "V2596" "V2597" "V2598" "V2599" "V2600" "V2601"
## [2602] "V2602" "V2603" "V2604" "V2605" "V2606" "V2607" "V2608" "V2609" "V2610"
## [2611] "V2611" "V2612" "V2613" "V2614" "V2615" "V2616" "V2617" "V2618" "V2619"
## [2620] "V2620" "V2621" "V2622" "V2623" "V2624" "V2625" "V2626" "V2627" "V2628"
## [2629] "V2629" "V2630" "V2631" "V2632" "V2633" "V2634" "V2635" "V2636" "V2637"
## [2638] "V2638" "V2639" "V2640" "V2641" "V2642" "V2643" "V2644" "V2645" "V2646"
## [2647] "V2647" "V2648" "V2649" "V2650" "V2651" "V2652" "V2653" "V2654" "V2655"
## [2656] "V2656" "V2657" "V2658" "V2659" "V2660" "V2661" "V2662" "V2663" "V2664"
## [2665] "V2665" "V2666" "V2667" "V2668" "V2669" "V2670" "V2671" "V2672" "V2673"
## [2674] "V2674" "V2675" "V2676" "V2677" "V2678" "V2679" "V2680" "V2681" "V2682"
## [2683] "V2683" "V2684" "V2685" "V2686" "V2687" "V2688" "V2689" "V2690" "V2691"
## [2692] "V2692" "V2693" "V2694" "V2695" "V2696" "V2697" "V2698" "V2699" "V2700"
## [2701] "V2701" "V2702" "V2703" "V2704" "V2705" "V2706" "V2707" "V2708" "V2709"
## [2710] "V2710" "V2711" "V2712" "V2713" "V2714" "V2715" "V2716" "V2717" "V2718"
## [2719] "V2719" "V2720" "V2721" "V2722" "V2723" "V2724" "V2725" "V2726" "V2727"
## [2728] "V2728" "V2729" "V2730" "V2731" "V2732" "V2733" "V2734" "V2735" "V2736"
## [2737] "V2737" "V2738" "V2739" "V2740" "V2741" "V2742" "V2743" "V2744" "V2745"
## [2746] "V2746" "V2747" "V2748" "V2749" "V2750" "V2751" "V2752" "V2753" "V2754"
## [2755] "V2755" "V2756" "V2757" "V2758" "V2759" "V2760" "V2761" "V2762" "V2763"
## [2764] "V2764" "V2765" "V2766" "V2767" "V2768" "V2769" "V2770" "V2771" "V2772"
## [2773] "V2773" "V2774" "V2775" "V2776" "V2777" "V2778" "V2779" "V2780" "V2781"
## [2782] "V2782" "V2783" "V2784" "V2785" "V2786" "V2787" "V2788" "V2789" "V2790"
## [2791] "V2791" "V2792" "V2793" "V2794" "V2795" "V2796" "V2797" "V2798" "V2799"
## [2800] "V2800" "V2801" "V2802" "V2803" "V2804" "V2805" "V2806" "V2807" "V2808"
## [2809] "V2809" "V2810" "V2811" "V2812" "V2813" "V2814" "V2815" "V2816" "V2817"
## [2818] "V2818" "V2819" "V2820" "V2821" "V2822" "V2823" "V2824" "V2825" "V2826"
## [2827] "V2827" "V2828" "V2829" "V2830" "V2831" "V2832" "V2833" "V2834" "V2835"
## [2836] "V2836" "V2837" "V2838" "V2839" "V2840" "V2841" "V2842" "V2843" "V2844"
## [2845] "V2845" "V2846" "V2847" "V2848" "V2849" "V2850" "V2851" "V2852" "V2853"
## [2854] "V2854" "V2855" "V2856" "V2857" "V2858" "V2859" "V2860" "V2861" "V2862"
## [2863] "V2863" "V2864" "V2865" "V2866" "V2867" "V2868" "V2869" "V2870" "V2871"
## [2872] "V2872" "V2873" "V2874" "V2875" "V2876" "V2877" "V2878" "V2879" "V2880"
## [2881] "V2881" "V2882" "V2883" "V2884" "V2885" "V2886" "V2887" "V2888" "V2889"
## [2890] "V2890" "V2891" "V2892" "V2893" "V2894" "V2895" "V2896" "V2897" "V2898"
## [2899] "V2899" "V2900" "V2901" "V2902" "V2903" "V2904" "V2905" "V2906" "V2907"
## [2908] "V2908" "V2909" "V2910" "V2911" "V2912" "V2913" "V2914" "V2915" "V2916"
## [2917] "V2917" "V2918" "V2919" "V2920" "V2921" "V2922" "V2923" "V2924" "V2925"
## [2926] "V2926" "V2927" "V2928" "V2929" "V2930" "V2931" "V2932" "V2933" "V2934"
## [2935] "V2935" "V2936" "V2937" "V2938" "V2939" "V2940" "V2941" "V2942" "V2943"
## [2944] "V2944" "V2945" "V2946" "V2947" "V2948" "V2949" "V2950" "V2951" "V2952"
## [2953] "V2953" "V2954" "V2955" "V2956" "V2957" "V2958" "V2959" "V2960" "V2961"
## [2962] "V2962" "V2963" "V2964" "V2965" "V2966" "V2967" "V2968" "V2969" "V2970"
## [2971] "V2971" "V2972" "V2973" "V2974" "V2975" "V2976" "V2977" "V2978" "V2979"
## [2980] "V2980" "V2981" "V2982" "V2983" "V2984" "V2985" "V2986" "V2987" "V2988"
## [2989] "V2989" "V2990" "V2991" "V2992" "V2993" "V2994" "V2995" "V2996" "V2997"
## [2998] "V2998" "V2999" "V3000" "V3001" "V3002" "V3003" "V3004" "V3005" "V3006"
## [3007] "V3007" "V3008" "V3009" "V3010" "V3011" "V3012" "V3013" "V3014" "V3015"
## [3016] "V3016" "V3017" "V3018" "V3019" "V3020" "V3021" "V3022" "V3023" "V3024"
## [3025] "V3025" "V3026" "V3027" "V3028" "V3029" "V3030" "V3031" "V3032" "V3033"
## [3034] "V3034" "V3035" "V3036" "V3037" "V3038" "V3039" "V3040" "V3041" "V3042"
## [3043] "V3043" "V3044" "V3045" "V3046" "V3047" "V3048" "V3049" "V3050" "V3051"
## [3052] "V3052" "V3053" "V3054" "V3055" "V3056" "V3057" "V3058" "V3059" "V3060"
## [3061] "V3061" "V3062" "V3063" "V3064" "V3065" "V3066" "V3067" "V3068" "V3069"
## [3070] "V3070" "V3071" "V3072" "V3073" "V3074" "V3075" "V3076" "V3077" "V3078"
## [3079] "V3079" "V3080" "V3081" "V3082" "V3083" "V3084" "V3085" "V3086" "V3087"
## [3088] "V3088" "V3089" "V3090" "V3091" "V3092" "V3093" "V3094" "V3095" "V3096"
## [3097] "V3097" "V3098" "V3099" "V3100" "V3101" "V3102" "V3103" "V3104" "V3105"
## [3106] "V3106" "V3107" "V3108" "V3109" "V3110" "V3111" "V3112" "V3113" "V3114"
## [3115] "V3115" "V3116" "V3117" "V3118" "V3119" "V3120" "V3121" "V3122" "V3123"
## [3124] "V3124" "V3125" "V3126" "V3127" "V3128" "V3129" "V3130" "V3131" "V3132"
## [3133] "V3133" "V3134" "V3135" "V3136" "V3137" "V3138" "V3139" "V3140" "V3141"
## [3142] "V3142" "V3143" "V3144" "V3145" "V3146" "V3147" "V3148" "V3149" "V3150"
## [3151] "V3151" "V3152" "V3153" "V3154" "V3155" "V3156" "V3157" "V3158" "V3159"
## [3160] "V3160" "V3161" "V3162" "V3163" "V3164" "V3165" "V3166" "V3167" "V3168"
## [3169] "V3169" "V3170" "V3171" "V3172" "V3173" "V3174" "V3175" "V3176" "V3177"
## [3178] "V3178" "V3179" "V3180" "V3181" "V3182" "V3183" "V3184" "V3185" "V3186"
## [3187] "V3187" "V3188" "V3189" "V3190" "V3191" "V3192" "V3193" "V3194" "V3195"
## [3196] "V3196" "V3197" "V3198" "V3199" "V3200" "V3201" "V3202" "V3203" "V3204"
## [3205] "V3205" "V3206" "V3207" "V3208" "V3209" "V3210" "V3211" "V3212" "V3213"
## [3214] "V3214" "V3215" "V3216" "V3217" "V3218" "V3219" "V3220" "V3221" "V3222"
## [3223] "V3223" "V3224" "V3225" "V3226" "V3227" "V3228" "V3229" "V3230" "V3231"
## [3232] "V3232" "V3233" "V3234" "V3235" "V3236" "V3237" "V3238" "V3239" "V3240"
## [3241] "V3241" "V3242" "V3243" "V3244" "V3245" "V3246" "V3247" "V3248" "V3249"
## [3250] "V3250" "V3251" "V3252" "V3253" "V3254" "V3255" "V3256" "V3257" "V3258"
## [3259] "V3259" "V3260" "V3261" "V3262" "V3263" "V3264" "V3265" "V3266" "V3267"
## [3268] "V3268" "V3269" "V3270" "V3271" "V3272" "V3273" "V3274" "V3275" "V3276"
## [3277] "V3277" "V3278" "V3279" "V3280" "V3281" "V3282" "V3283" "V3284" "V3285"
## [3286] "V3286" "V3287" "V3288" "V3289" "V3290" "V3291" "V3292" "V3293" "V3294"
## [3295] "V3295" "V3296" "V3297" "V3298" "V3299" "V3300" "V3301" "V3302" "V3303"
## [3304] "V3304" "V3305" "V3306" "V3307" "V3308" "V3309" "V3310" "V3311" "V3312"
## [3313] "V3313" "V3314" "V3315" "V3316" "V3317" "V3318" "V3319" "V3320" "V3321"
## [3322] "V3322" "V3323" "V3324" "V3325" "V3326" "V3327" "V3328" "V3329" "V3330"
## [3331] "V3331" "V3332" "V3333" "V3334" "V3335" "V3336" "V3337" "V3338" "V3339"
## [3340] "V3340" "V3341" "V3342" "V3343" "V3344" "V3345" "V3346" "V3347" "V3348"
## [3349] "V3349" "V3350" "V3351" "V3352" "V3353" "V3354" "V3355" "V3356" "V3357"
## [3358] "V3358" "V3359" "V3360" "V3361" "V3362" "V3363" "V3364" "V3365" "V3366"
## [3367] "V3367" "V3368" "V3369" "V3370" "V3371" "V3372" "V3373" "V3374" "V3375"
## [3376] "V3376" "V3377" "V3378" "V3379" "V3380" "V3381" "V3382" "V3383" "V3384"
## [3385] "V3385" "V3386" "V3387" "V3388" "V3389" "V3390" "V3391" "V3392" "V3393"
## [3394] "V3394" "V3395" "V3396" "V3397" "V3398" "V3399" "V3400" "V3401" "V3402"
## [3403] "V3403" "V3404" "V3405" "V3406" "V3407" "V3408" "V3409" "V3410" "V3411"
## [3412] "V3412" "V3413" "V3414" "V3415" "V3416" "V3417" "V3418" "V3419" "V3420"
## [3421] "V3421" "V3422" "V3423" "V3424" "V3425" "V3426" "V3427" "V3428" "V3429"
## [3430] "V3430" "V3431" "V3432" "V3433" "V3434" "V3435" "V3436" "V3437" "V3438"
## [3439] "V3439" "V3440" "V3441" "V3442" "V3443" "V3444" "V3445" "V3446" "V3447"
## [3448] "V3448" "V3449" "V3450" "V3451" "V3452" "V3453" "V3454" "V3455" "V3456"
## [3457] "V3457" "V3458" "V3459" "V3460" "V3461" "V3462" "V3463" "V3464" "V3465"
## [3466] "V3466" "V3467" "V3468" "V3469" "V3470" "V3471" "V3472" "V3473" "V3474"
## [3475] "V3475" "V3476" "V3477" "V3478" "V3479" "V3480" "V3481" "V3482" "V3483"
## [3484] "V3484" "V3485" "V3486" "V3487" "V3488" "V3489" "V3490" "V3491" "V3492"
## [3493] "V3493" "V3494" "V3495" "V3496" "V3497" "V3498" "V3499" "V3500" "V3501"
## [3502] "V3502" "V3503" "V3504" "V3505" "V3506" "V3507" "V3508" "V3509" "V3510"
## [3511] "V3511" "V3512" "V3513" "V3514" "V3515" "V3516" "V3517" "V3518" "V3519"
## [3520] "V3520" "V3521" "V3522" "V3523" "V3524" "V3525" "V3526" "V3527" "V3528"
## [3529] "V3529" "V3530" "V3531" "V3532" "V3533" "V3534" "V3535" "V3536" "V3537"
## [3538] "V3538" "V3539" "V3540" "V3541" "V3542" "V3543" "V3544" "V3545" "V3546"
## [3547] "V3547" "V3548" "V3549" "V3550" "V3551" "V3552" "V3553" "V3554" "V3555"
## [3556] "V3556" "V3557" "V3558" "V3559" "V3560" "V3561" "V3562" "V3563" "V3564"
## [3565] "V3565" "V3566" "V3567" "V3568" "V3569" "V3570" "V3571" "V3572" "V3573"
## [3574] "V3574" "V3575" "V3576" "V3577" "V3578" "V3579" "V3580" "V3581" "V3582"
## [3583] "V3583" "V3584" "V3585" "V3586" "V3587" "V3588" "V3589" "V3590" "V3591"
## [3592] "V3592" "V3593" "V3594" "V3595" "V3596" "V3597" "V3598" "V3599" "V3600"
## [3601] "V3601" "V3602" "V3603" "V3604" "V3605" "V3606" "V3607" "V3608" "V3609"
## [3610] "V3610" "V3611" "V3612" "V3613" "V3614" "V3615" "V3616" "V3617" "V3618"
## [3619] "V3619" "V3620" "V3621" "V3622" "V3623" "V3624" "V3625" "V3626" "V3627"
## [3628] "V3628" "V3629" "V3630" "V3631" "V3632" "V3633" "V3634" "V3635" "V3636"
## [3637] "V3637" "V3638" "V3639" "V3640" "V3641" "V3642" "V3643" "V3644" "V3645"
## [3646] "V3646" "V3647" "V3648" "V3649" "V3650" "V3651" "V3652" "V3653" "V3654"
## [3655] "V3655" "V3656" "V3657" "V3658" "V3659" "V3660" "V3661" "V3662" "V3663"
## [3664] "V3664" "V3665" "V3666" "V3667" "V3668" "V3669" "V3670" "V3671" "V3672"
## [3673] "V3673" "V3674" "V3675" "V3676" "V3677" "V3678" "V3679" "V3680" "V3681"
## [3682] "V3682" "V3683" "V3684" "V3685" "V3686" "V3687" "V3688" "V3689" "V3690"
## [3691] "V3691" "V3692" "V3693" "V3694" "V3695" "V3696" "V3697" "V3698" "V3699"
## [3700] "V3700" "V3701" "V3702" "V3703" "V3704" "V3705" "V3706" "V3707" "V3708"
## [3709] "V3709" "V3710" "V3711" "V3712" "V3713" "V3714" "V3715" "V3716" "V3717"
## [3718] "V3718" "V3719" "V3720" "V3721" "V3722" "V3723" "V3724" "V3725" "V3726"
## [3727] "V3727" "V3728" "V3729" "V3730" "V3731" "V3732" "V3733" "V3734" "V3735"
## [3736] "V3736" "V3737" "V3738" "V3739" "V3740" "V3741" "V3742" "V3743" "V3744"
## [3745] "V3745" "V3746" "V3747" "V3748" "V3749" "V3750" "V3751" "V3752" "V3753"
## [3754] "V3754" "V3755" "V3756" "V3757" "V3758" "V3759" "V3760" "V3761" "V3762"
## [3763] "V3763" "V3764" "V3765" "V3766" "V3767" "V3768" "V3769" "V3770" "V3771"
## [3772] "V3772" "V3773" "V3774" "V3775" "V3776" "V3777" "V3778" "V3779" "V3780"
## [3781] "V3781" "V3782" "V3783" "V3784" "V3785" "V3786" "V3787" "V3788" "V3789"
## [3790] "V3790" "V3791" "V3792" "V3793" "V3794" "V3795" "V3796" "V3797" "V3798"
## [3799] "V3799" "V3800" "V3801" "V3802" "V3803" "V3804" "V3805" "V3806" "V3807"
## [3808] "V3808" "V3809" "V3810" "V3811" "V3812" "V3813" "V3814" "V3815" "V3816"
## [3817] "V3817" "V3818" "V3819" "V3820" "V3821" "V3822" "V3823" "V3824" "V3825"
## [3826] "V3826" "V3827" "V3828" "V3829" "V3830" "V3831" "V3832" "V3833" "V3834"
## [3835] "V3835" "V3836" "V3837" "V3838" "V3839" "V3840" "V3841" "V3842" "V3843"
## [3844] "V3844" "V3845" "V3846" "V3847" "V3848" "V3849" "V3850" "V3851" "V3852"
## [3853] "V3853" "V3854" "V3855" "V3856" "V3857" "V3858" "V3859" "V3860" "V3861"
## [3862] "V3862" "V3863" "V3864" "V3865" "V3866" "V3867" "V3868" "V3869" "V3870"
## [3871] "V3871" "V3872" "V3873" "V3874" "V3875" "V3876" "V3877" "V3878" "V3879"
## [3880] "V3880" "V3881" "V3882" "V3883" "V3884" "V3885" "V3886" "V3887" "V3888"
## [3889] "V3889" "V3890" "V3891" "V3892" "V3893" "V3894" "V3895" "V3896" "V3897"
## [3898] "V3898" "V3899" "V3900" "V3901" "V3902" "V3903" "V3904" "V3905" "V3906"
## [3907] "V3907" "V3908" "V3909" "V3910" "V3911" "V3912" "V3913" "V3914" "V3915"
## [3916] "V3916" "V3917" "V3918" "V3919" "V3920" "V3921" "V3922" "V3923" "V3924"
## [3925] "V3925" "V3926" "V3927" "V3928" "V3929" "V3930" "V3931" "V3932" "V3933"
## [3934] "V3934" "V3935" "V3936" "V3937" "V3938" "V3939" "V3940" "V3941" "V3942"
## [3943] "V3943" "V3944" "V3945" "V3946" "V3947" "V3948" "V3949" "V3950" "V3951"
## [3952] "V3952" "V3953" "V3954" "V3955" "V3956" "V3957" "V3958" "V3959" "V3960"
## [3961] "V3961" "V3962" "V3963" "V3964" "V3965" "V3966" "V3967" "V3968" "V3969"
## [3970] "V3970" "V3971" "V3972" "V3973" "V3974" "V3975" "V3976" "V3977" "V3978"
## [3979] "V3979" "V3980" "V3981" "V3982" "V3983" "V3984" "V3985" "V3986" "V3987"
## [3988] "V3988" "V3989" "V3990" "V3991" "V3992" "V3993" "V3994" "V3995" "V3996"
## [3997] "V3997" "V3998" "V3999" "V4000" "V4001" "V4002" "V4003" "V4004" "V4005"
## [4006] "V4006" "V4007" "V4008" "V4009" "V4010" "V4011" "V4012" "V4013" "V4014"
## [4015] "V4015" "V4016" "V4017" "V4018" "V4019" "V4020" "V4021" "V4022" "V4023"
## [4024] "V4024" "V4025" "V4026" "V4027" "V4028" "V4029" "V4030" "V4031" "V4032"
## [4033] "V4033" "V4034" "V4035" "V4036" "V4037" "V4038" "V4039" "V4040" "V4041"
## [4042] "V4042" "V4043" "V4044" "V4045" "V4046" "V4047" "V4048" "V4049" "V4050"
## [4051] "V4051" "V4052" "V4053" "V4054" "V4055" "V4056" "V4057" "V4058" "V4059"
## [4060] "V4060" "V4061" "V4062" "V4063" "V4064" "V4065" "V4066" "V4067" "V4068"
## [4069] "V4069" "V4070" "V4071" "V4072" "V4073" "V4074" "V4075" "V4076" "V4077"
## [4078] "V4078" "V4079" "V4080" "V4081" "V4082" "V4083" "V4084" "V4085" "V4086"
## [4087] "V4087" "V4088" "V4089" "V4090" "V4091" "V4092" "V4093" "V4094" "V4095"
## [4096] "V4096" "V4097" "V4098" "V4099" "V4100" "V4101" "V4102" "V4103" "V4104"
## [4105] "V4105" "V4106" "V4107" "V4108" "V4109" "V4110" "V4111" "V4112" "V4113"
## [4114] "V4114" "V4115" "V4116" "V4117" "V4118" "V4119" "V4120" "V4121" "V4122"
## [4123] "V4123" "V4124" "V4125" "V4126" "V4127" "V4128" "V4129" "V4130" "V4131"
## [4132] "V4132" "V4133" "V4134" "V4135" "V4136" "V4137" "V4138" "V4139" "V4140"
## [4141] "V4141" "V4142" "V4143" "V4144" "V4145" "V4146" "V4147" "V4148" "V4149"
## [4150] "V4150" "V4151" "V4152" "V4153" "V4154" "V4155" "V4156" "V4157" "V4158"
## [4159] "V4159" "V4160" "V4161" "V4162" "V4163" "V4164" "V4165" "V4166" "V4167"
## [4168] "V4168" "V4169" "V4170" "V4171" "V4172" "V4173" "V4174" "V4175" "V4176"
## [4177] "V4177" "V4178" "V4179" "V4180" "V4181" "V4182" "V4183" "V4184" "V4185"
## [4186] "V4186" "V4187" "V4188" "V4189" "V4190" "V4191" "V4192" "V4193" "V4194"
## [4195] "V4195" "V4196" "V4197" "V4198" "V4199" "V4200" "V4201" "V4202" "V4203"
## [4204] "V4204" "V4205" "V4206" "V4207" "V4208" "V4209" "V4210" "V4211" "V4212"
## [4213] "V4213" "V4214" "V4215" "V4216" "V4217" "V4218" "V4219" "V4220" "V4221"
## [4222] "V4222" "V4223" "V4224" "V4225" "V4226" "V4227" "V4228" "V4229" "V4230"
## [4231] "V4231" "V4232" "V4233" "V4234" "V4235" "V4236" "V4237" "V4238" "V4239"
## [4240] "V4240" "V4241" "V4242" "V4243" "V4244" "V4245" "V4246" "V4247" "V4248"
## [4249] "V4249" "V4250" "V4251" "V4252" "V4253" "V4254" "V4255" "V4256" "V4257"
## [4258] "V4258" "V4259" "V4260" "V4261" "V4262" "V4263" "V4264" "V4265" "V4266"
## [4267] "V4267" "V4268" "V4269" "V4270" "V4271" "V4272" "V4273" "V4274" "V4275"
## [4276] "V4276" "V4277" "V4278" "V4279" "V4280" "V4281" "V4282" "V4283" "V4284"
## [4285] "V4285" "V4286" "V4287" "V4288" "V4289" "V4290" "V4291" "V4292" "V4293"
## [4294] "V4294" "V4295" "V4296" "V4297" "V4298" "V4299" "V4300" "V4301" "V4302"
## [4303] "V4303" "V4304" "V4305" "V4306" "V4307" "V4308" "V4309" "V4310" "V4311"
## [4312] "V4312" "V4313" "V4314" "V4315" "V4316" "V4317" "V4318" "V4319" "V4320"
## [4321] "V4321" "V4322" "V4323" "V4324" "V4325" "V4326" "V4327" "V4328" "V4329"
## [4330] "V4330" "V4331" "V4332" "V4333" "V4334" "V4335" "V4336" "V4337" "V4338"
## [4339] "V4339" "V4340" "V4341" "V4342" "V4343" "V4344" "V4345" "V4346" "V4347"
## [4348] "V4348" "V4349" "V4350" "V4351" "V4352" "V4353" "V4354" "V4355" "V4356"
## [4357] "V4357" "V4358" "V4359" "V4360" "V4361" "V4362" "V4363" "V4364" "V4365"
## [4366] "V4366" "V4367" "V4368" "V4369" "V4370" "V4371" "V4372" "V4373" "V4374"
## [4375] "V4375" "V4376" "V4377" "V4378" "V4379" "V4380" "V4381" "V4382" "V4383"
## [4384] "V4384" "V4385" "V4386" "V4387" "V4388" "V4389" "V4390" "V4391" "V4392"
## [4393] "V4393" "V4394" "V4395" "V4396" "V4397" "V4398" "V4399" "V4400" "V4401"
## [4402] "V4402" "V4403" "V4404" "V4405" "V4406" "V4407" "V4408" "V4409" "V4410"
## [4411] "V4411" "V4412" "V4413" "V4414" "V4415" "V4416" "V4417" "V4418" "V4419"
## [4420] "V4420" "V4421" "V4422" "V4423" "V4424" "V4425" "V4426" "V4427" "V4428"
## [4429] "V4429" "V4430" "V4431" "V4432" "V4433" "V4434" "V4435" "V4436" "V4437"
## [4438] "V4438" "V4439" "V4440" "V4441" "V4442" "V4443" "V4444" "V4445" "V4446"
## [4447] "V4447" "V4448" "V4449" "V4450" "V4451" "V4452" "V4453" "V4454" "V4455"
## [4456] "V4456" "V4457" "V4458" "V4459" "V4460" "V4461" "V4462" "V4463" "V4464"
## [4465] "V4465" "V4466" "V4467" "V4468" "V4469" "V4470" "V4471" "V4472" "V4473"
## [4474] "V4474" "V4475" "V4476" "V4477" "V4478" "V4479" "V4480" "V4481" "V4482"
## [4483] "V4483" "V4484" "V4485" "V4486" "V4487" "V4488" "V4489" "V4490" "V4491"
## [4492] "V4492" "V4493" "V4494" "V4495" "V4496" "V4497" "V4498" "V4499" "V4500"
## [4501] "V4501" "V4502" "V4503" "V4504" "V4505" "V4506" "V4507" "V4508" "V4509"
## [4510] "V4510" "V4511" "V4512" "V4513" "V4514" "V4515" "V4516" "V4517" "V4518"
## [4519] "V4519" "V4520" "V4521" "V4522" "V4523" "V4524" "V4525" "V4526" "V4527"
## [4528] "V4528" "V4529" "V4530" "V4531" "V4532" "V4533" "V4534" "V4535" "V4536"
## [4537] "V4537" "V4538" "V4539" "V4540" "V4541" "V4542" "V4543" "V4544" "V4545"
## [4546] "V4546" "V4547" "V4548" "V4549" "V4550" "V4551" "V4552" "V4553" "V4554"
## [4555] "V4555" "V4556" "V4557" "V4558" "V4559" "V4560" "V4561" "V4562" "V4563"
## [4564] "V4564" "V4565" "V4566" "V4567" "V4568" "V4569" "V4570" "V4571" "V4572"
## [4573] "V4573" "V4574" "V4575" "V4576" "V4577" "V4578" "V4579" "V4580" "V4581"
## [4582] "V4582" "V4583" "V4584" "V4585" "V4586" "V4587" "V4588" "V4589" "V4590"
## [4591] "V4591" "V4592" "V4593" "V4594" "V4595" "V4596" "V4597" "V4598" "V4599"
## [4600] "V4600" "V4601" "V4602" "V4603" "V4604" "V4605" "V4606" "V4607" "V4608"
## [4609] "V4609" "V4610" "V4611" "V4612" "V4613" "V4614" "V4615" "V4616" "V4617"
## [4618] "V4618" "V4619" "V4620" "V4621" "V4622" "V4623" "V4624" "V4625" "V4626"
## [4627] "V4627" "V4628" "V4629" "V4630" "V4631" "V4632" "V4633" "V4634" "V4635"
## [4636] "V4636" "V4637" "V4638" "V4639" "V4640" "V4641" "V4642" "V4643" "V4644"
## [4645] "V4645" "V4646" "V4647" "V4648" "V4649" "V4650" "V4651" "V4652" "V4653"
## [4654] "V4654" "V4655" "V4656" "V4657" "V4658" "V4659" "V4660" "V4661" "V4662"
## [4663] "V4663" "V4664" "V4665" "V4666" "V4667" "V4668" "V4669" "V4670" "V4671"
## [4672] "V4672" "V4673" "V4674" "V4675" "V4676" "V4677" "V4678" "V4679" "V4680"
## [4681] "V4681" "V4682" "V4683" "V4684" "V4685" "V4686" "V4687" "V4688" "V4689"
## [4690] "V4690" "V4691" "V4692" "V4693" "V4694" "V4695" "V4696" "V4697" "V4698"
## [4699] "V4699" "V4700" "V4701" "V4702" "V4703" "V4704" "V4705" "V4706" "V4707"
## [4708] "V4708" "V4709" "V4710" "V4711" "V4712" "V4713" "V4714" "V4715" "V4716"
## [4717] "V4717" "V4718" "V4719" "V4720" "V4721" "V4722" "V4723" "V4724" "V4725"
## [4726] "V4726" "V4727" "V4728" "V4729" "V4730" "V4731" "V4732" "V4733" "V4734"
## [4735] "V4735" "V4736" "V4737" "V4738" "V4739" "V4740" "V4741" "V4742" "V4743"
## [4744] "V4744" "V4745" "V4746" "V4747" "V4748" "V4749" "V4750" "V4751" "V4752"
## [4753] "V4753" "V4754" "V4755" "V4756" "V4757" "V4758" "V4759" "V4760" "V4761"
## [4762] "V4762" "V4763" "V4764" "V4765" "V4766" "V4767" "V4768" "V4769" "V4770"
## [4771] "V4771" "V4772" "V4773" "V4774" "V4775" "V4776" "V4777" "V4778" "V4779"
## [4780] "V4780" "V4781" "V4782" "V4783" "V4784" "V4785" "V4786" "V4787" "V4788"
## [4789] "V4789" "V4790" "V4791" "V4792" "V4793" "V4794" "V4795" "V4796" "V4797"
## [4798] "V4798" "V4799" "V4800" "V4801" "V4802" "V4803" "V4804" "V4805" "V4806"
## [4807] "V4807" "V4808" "V4809" "V4810" "V4811" "V4812" "V4813" "V4814" "V4815"
## [4816] "V4816" "V4817" "V4818" "V4819" "V4820" "V4821" "V4822" "V4823" "V4824"
## [4825] "V4825" "V4826" "V4827" "V4828" "V4829" "V4830" "V4831" "V4832" "V4833"
## [4834] "V4834" "V4835" "V4836" "V4837" "V4838" "V4839" "V4840" "V4841" "V4842"
## [4843] "V4843" "V4844" "V4845" "V4846" "V4847" "V4848" "V4849" "V4850" "V4851"
## [4852] "V4852" "V4853" "V4854" "V4855" "V4856" "V4857" "V4858" "V4859" "V4860"
## [4861] "V4861" "V4862" "V4863" "V4864" "V4865" "V4866" "V4867" "V4868" "V4869"
## [4870] "V4870" "V4871" "V4872" "V4873" "V4874" "V4875" "V4876" "V4877" "V4878"
## [4879] "V4879" "V4880" "V4881" "V4882" "V4883" "V4884" "V4885" "V4886" "V4887"
## [4888] "V4888" "V4889" "V4890" "V4891" "V4892" "V4893" "V4894" "V4895" "V4896"
## [4897] "V4897" "V4898" "V4899" "V4900" "V4901" "V4902" "V4903" "V4904" "V4905"
## [4906] "V4906" "V4907" "V4908" "V4909" "V4910" "V4911" "V4912" "V4913" "V4914"
## [4915] "V4915" "V4916" "V4917" "V4918" "V4919" "V4920" "V4921" "V4922" "V4923"
## [4924] "V4924" "V4925" "V4926" "V4927" "V4928" "V4929" "V4930" "V4931" "V4932"
## [4933] "V4933" "V4934" "V4935" "V4936" "V4937" "V4938" "V4939" "V4940" "V4941"
## [4942] "V4942" "V4943" "V4944" "V4945" "V4946" "V4947" "V4948" "V4949" "V4950"
## [4951] "V4951" "V4952" "V4953" "V4954" "V4955" "V4956" "V4957" "V4958" "V4959"
## [4960] "V4960" "V4961" "V4962" "V4963" "V4964" "V4965" "V4966" "V4967" "V4968"
## [4969] "V4969" "V4970" "V4971" "V4972" "V4973" "V4974" "V4975" "V4976" "V4977"
## [4978] "V4978" "V4979" "V4980" "V4981" "V4982" "V4983" "V4984" "V4985" "V4986"
## [4987] "V4987" "V4988" "V4989" "V4990" "V4991" "V4992" "V4993" "V4994" "V4995"
## [4996] "V4996" "V4997" "V4998" "V4999" "V5000" "V5001" "V5002" "V5003" "V5004"
## [5005] "V5005" "V5006" "V5007" "V5008" "V5009" "V5010" "V5011" "V5012" "V5013"
## [5014] "V5014" "V5015" "V5016" "V5017" "V5018" "V5019" "V5020" "V5021" "V5022"
## [5023] "V5023" "V5024" "V5025" "V5026" "V5027" "V5028" "V5029" "V5030" "V5031"
## [5032] "V5032" "V5033" "V5034" "V5035" "V5036" "V5037" "V5038" "V5039" "V5040"
## [5041] "V5041" "V5042" "V5043" "V5044" "V5045" "V5046" "V5047" "V5048" "V5049"
## [5050] "V5050" "V5051" "V5052" "V5053" "V5054" "V5055" "V5056" "V5057" "V5058"
## [5059] "V5059" "V5060" "V5061" "V5062" "V5063" "V5064" "V5065" "V5066" "V5067"
## [5068] "V5068" "V5069" "V5070" "V5071" "V5072" "V5073" "V5074" "V5075" "V5076"
## [5077] "V5077" "V5078" "V5079" "V5080" "V5081" "V5082" "V5083" "V5084" "V5085"
## [5086] "V5086" "V5087" "V5088" "V5089" "V5090" "V5091" "V5092" "V5093" "V5094"
## [5095] "V5095" "V5096" "V5097" "V5098" "V5099" "V5100" "V5101" "V5102" "V5103"
## [5104] "V5104" "V5105" "V5106" "V5107" "V5108" "V5109" "V5110" "V5111" "V5112"
## [5113] "V5113" "V5114" "V5115" "V5116" "V5117" "V5118" "V5119" "V5120" "V5121"
## [5122] "V5122" "V5123" "V5124" "V5125" "V5126" "V5127" "V5128" "V5129" "V5130"
## [5131] "V5131" "V5132" "V5133" "V5134" "V5135" "V5136" "V5137" "V5138" "V5139"
## [5140] "V5140" "V5141" "V5142" "V5143" "V5144" "V5145" "V5146" "V5147" "V5148"
## [5149] "V5149" "V5150" "V5151" "V5152" "V5153" "V5154" "V5155" "V5156" "V5157"
## [5158] "V5158" "V5159" "V5160" "V5161" "V5162" "V5163" "V5164" "V5165" "V5166"
## [5167] "V5167" "V5168" "V5169" "V5170" "V5171" "V5172" "V5173" "V5174" "V5175"
## [5176] "V5176" "V5177" "V5178" "V5179" "V5180" "V5181" "V5182" "V5183" "V5184"
## [5185] "V5185" "V5186" "V5187" "V5188" "V5189" "V5190" "V5191" "V5192" "V5193"
## [5194] "V5194" "V5195" "V5196" "V5197" "V5198" "V5199" "V5200" "V5201" "V5202"
## [5203] "V5203" "V5204" "V5205" "V5206" "V5207" "V5208" "V5209" "V5210" "V5211"
## [5212] "V5212" "V5213" "V5214" "V5215" "V5216" "V5217" "V5218" "V5219" "V5220"
## [5221] "V5221" "V5222" "V5223" "V5224" "V5225" "V5226" "V5227" "V5228" "V5229"
## [5230] "V5230" "V5231" "V5232" "V5233" "V5234" "V5235" "V5236" "V5237" "V5238"
## [5239] "V5239" "V5240" "V5241" "V5242" "V5243" "V5244" "V5245" "V5246" "V5247"
## [5248] "V5248" "V5249" "V5250" "V5251" "V5252" "V5253" "V5254" "V5255" "V5256"
## [5257] "V5257" "V5258" "V5259" "V5260" "V5261" "V5262" "V5263" "V5264" "V5265"
## [5266] "V5266" "V5267" "V5268" "V5269" "V5270" "V5271" "V5272" "V5273" "V5274"
## [5275] "V5275" "V5276" "V5277" "V5278" "V5279" "V5280" "V5281" "V5282" "V5283"
## [5284] "V5284" "V5285" "V5286" "V5287" "V5288" "V5289" "V5290" "V5291" "V5292"
## [5293] "V5293" "V5294" "V5295" "V5296" "V5297" "V5298" "V5299" "V5300" "V5301"
## [5302] "V5302" "V5303" "V5304" "V5305" "V5306" "V5307" "V5308" "V5309" "V5310"
## [5311] "V5311" "V5312" "V5313" "V5314" "V5315" "V5316" "V5317" "V5318" "V5319"
## [5320] "V5320" "V5321" "V5322" "V5323" "V5324" "V5325" "V5326" "V5327" "V5328"
## [5329] "V5329" "V5330" "V5331" "V5332" "V5333" "V5334" "V5335" "V5336" "V5337"
## [5338] "V5338" "V5339" "V5340" "V5341" "V5342" "V5343" "V5344" "V5345" "V5346"
## [5347] "V5347" "V5348" "V5349" "V5350" "V5351" "V5352" "V5353" "V5354" "V5355"
## [5356] "V5356" "V5357" "V5358" "V5359" "V5360" "V5361" "V5362" "V5363" "V5364"
## [5365] "V5365" "V5366" "V5367" "V5368" "V5369" "V5370" "V5371" "V5372" "V5373"
## [5374] "V5374" "V5375" "V5376" "V5377" "V5378" "V5379" "V5380" "V5381" "V5382"
## [5383] "V5383" "V5384" "V5385" "V5386" "V5387" "V5388" "V5389" "V5390" "V5391"
## [5392] "V5392" "V5393" "V5394" "V5395" "V5396" "V5397" "V5398" "V5399" "V5400"
## [5401] "V5401" "V5402" "V5403" "V5404" "V5405" "V5406" "V5407" "V5408" "V5409"
## [5410] "V5410" "V5411" "V5412" "V5413" "V5414" "V5415" "V5416" "V5417" "V5418"
## [5419] "V5419" "V5420" "V5421" "V5422" "V5423" "V5424" "V5425" "V5426" "V5427"
## [5428] "V5428" "V5429" "V5430" "V5431" "V5432" "V5433" "V5434" "V5435" "V5436"
## [5437] "V5437" "V5438" "V5439" "V5440" "V5441" "V5442" "V5443" "V5444" "V5445"
## [5446] "V5446" "V5447" "V5448" "V5449" "V5450" "V5451" "V5452" "V5453" "V5454"
## [5455] "V5455" "V5456" "V5457" "V5458" "V5459" "V5460" "V5461" "V5462" "V5463"
## [5464] "V5464" "V5465" "V5466" "V5467" "V5468" "V5469" "V5470" "V5471" "V5472"
## [5473] "V5473" "V5474" "V5475" "V5476" "V5477" "V5478" "V5479" "V5480" "V5481"
## [5482] "V5482" "V5483" "V5484" "V5485" "V5486" "V5487" "V5488" "V5489" "V5490"
## [5491] "V5491" "V5492" "V5493" "V5494" "V5495" "V5496" "V5497" "V5498" "V5499"
## [5500] "V5500" "V5501" "V5502" "V5503" "V5504" "V5505" "V5506" "V5507" "V5508"
## [5509] "V5509" "V5510" "V5511" "V5512" "V5513" "V5514" "V5515" "V5516" "V5517"
## [5518] "V5518" "V5519" "V5520" "V5521" "V5522" "V5523" "V5524" "V5525" "V5526"
## [5527] "V5527" "V5528" "V5529" "V5530" "V5531" "V5532" "V5533" "V5534" "V5535"
## [5536] "V5536" "V5537" "V5538" "V5539" "V5540" "V5541" "V5542" "V5543" "V5544"
## [5545] "V5545" "V5546" "V5547" "V5548" "V5549" "V5550" "V5551" "V5552" "V5553"
## [5554] "V5554" "V5555" "V5556" "V5557" "V5558" "V5559" "V5560" "V5561" "V5562"
## [5563] "V5563" "V5564" "V5565" "V5566" "V5567" "V5568" "V5569" "V5570" "V5571"
## [5572] "V5572" "V5573" "V5574" "V5575" "V5576" "V5577" "V5578" "V5579" "V5580"
## [5581] "V5581" "V5582" "V5583" "V5584" "V5585" "V5586" "V5587" "V5588" "V5589"
## [5590] "V5590" "V5591" "V5592" "V5593" "V5594" "V5595" "V5596" "V5597" "V5598"
## [5599] "V5599" "V5600" "V5601" "V5602" "V5603" "V5604" "V5605" "V5606" "V5607"
## [5608] "V5608" "V5609" "V5610" "V5611" "V5612" "V5613" "V5614" "V5615" "V5616"
## [5617] "V5617" "V5618" "V5619" "V5620" "V5621" "V5622" "V5623" "V5624" "V5625"
## [5626] "V5626" "V5627" "V5628" "V5629" "V5630" "V5631" "V5632" "V5633" "V5634"
## [5635] "V5635" "V5636" "V5637" "V5638" "V5639" "V5640" "V5641" "V5642" "V5643"
## [5644] "V5644" "V5645" "V5646" "V5647" "V5648" "V5649" "V5650" "V5651" "V5652"
## [5653] "V5653" "V5654" "V5655" "V5656" "V5657" "V5658" "V5659" "V5660" "V5661"
## [5662] "V5662" "V5663" "V5664" "V5665" "V5666" "V5667" "V5668" "V5669" "V5670"
## [5671] "V5671" "V5672" "V5673" "V5674" "V5675" "V5676" "V5677" "V5678" "V5679"
## [5680] "V5680" "V5681" "V5682" "V5683" "V5684" "V5685" "V5686" "V5687" "V5688"
## [5689] "V5689" "V5690" "V5691" "V5692" "V5693" "V5694" "V5695" "V5696" "V5697"
## [5698] "V5698" "V5699" "V5700" "V5701" "V5702" "V5703" "V5704" "V5705" "V5706"
## [5707] "V5707" "V5708" "V5709" "V5710" "V5711" "V5712" "V5713" "V5714" "V5715"
## [5716] "V5716" "V5717" "V5718" "V5719" "V5720" "V5721" "V5722" "V5723" "V5724"
## [5725] "V5725" "V5726" "V5727" "V5728" "V5729" "V5730" "V5731" "V5732" "V5733"
## [5734] "V5734" "V5735" "V5736" "V5737" "V5738" "V5739" "V5740" "V5741" "V5742"
## [5743] "V5743" "V5744" "V5745" "V5746" "V5747" "V5748" "V5749" "V5750" "V5751"
## [5752] "V5752" "V5753" "V5754" "V5755" "V5756" "V5757" "V5758" "V5759" "V5760"
## [5761] "V5761" "V5762" "V5763" "V5764" "V5765" "V5766" "V5767" "V5768" "V5769"
## [5770] "V5770" "V5771" "V5772" "V5773" "V5774" "V5775" "V5776" "V5777" "V5778"
## [5779] "V5779" "V5780" "V5781" "V5782" "V5783" "V5784" "V5785" "V5786" "V5787"
## [5788] "V5788" "V5789" "V5790" "V5791" "V5792" "V5793" "V5794" "V5795" "V5796"
## [5797] "V5797" "V5798" "V5799" "V5800" "V5801" "V5802" "V5803" "V5804" "V5805"
## [5806] "V5806" "V5807" "V5808" "V5809" "V5810" "V5811" "V5812" "V5813" "V5814"
## [5815] "V5815" "V5816" "V5817" "V5818" "V5819" "V5820" "V5821" "V5822" "V5823"
## [5824] "V5824" "V5825" "V5826" "V5827" "V5828" "V5829" "V5830" "V5831" "V5832"
## [5833] "V5833" "V5834" "V5835" "V5836" "V5837" "V5838" "V5839" "V5840" "V5841"
## [5842] "V5842" "V5843" "V5844" "V5845" "V5846" "V5847" "V5848" "V5849" "V5850"
## [5851] "V5851" "V5852" "V5853" "V5854" "V5855" "V5856" "V5857" "V5858" "V5859"
## [5860] "V5860" "V5861" "V5862" "V5863" "V5864" "V5865" "V5866" "V5867" "V5868"
## [5869] "V5869" "V5870" "V5871" "V5872" "V5873" "V5874" "V5875" "V5876" "V5877"
## [5878] "V5878" "V5879" "V5880" "V5881" "V5882" "V5883" "V5884" "V5885" "V5886"
## [5887] "V5887" "V5888" "V5889" "V5890" "V5891" "V5892" "V5893" "V5894" "V5895"
## [5896] "V5896" "V5897" "V5898" "V5899" "V5900" "V5901" "V5902" "V5903" "V5904"
## [5905] "V5905" "V5906" "V5907" "V5908" "V5909" "V5910" "V5911" "V5912" "V5913"
## [5914] "V5914" "V5915" "V5916" "V5917" "V5918" "V5919" "V5920" "V5921" "V5922"
## [5923] "V5923" "V5924" "V5925" "V5926" "V5927" "V5928" "V5929" "V5930" "V5931"
## [5932] "V5932" "V5933" "V5934" "V5935" "V5936" "V5937" "V5938" "V5939" "V5940"
## [5941] "V5941" "V5942" "V5943" "V5944" "V5945" "V5946" "V5947" "V5948" "V5949"
## [5950] "V5950" "V5951" "V5952" "V5953" "V5954" "V5955" "V5956" "V5957" "V5958"
## [5959] "V5959" "V5960" "V5961" "V5962" "V5963" "V5964" "V5965" "V5966" "V5967"
## [5968] "V5968" "V5969" "V5970" "V5971" "V5972" "V5973" "V5974" "V5975" "V5976"
## [5977] "V5977" "V5978" "V5979" "V5980" "V5981" "V5982" "V5983" "V5984" "V5985"
## [5986] "V5986" "V5987" "V5988" "V5989" "V5990" "V5991" "V5992" "V5993" "V5994"
## [5995] "V5995" "V5996" "V5997" "V5998" "V5999" "V6000" "V6001" "V6002" "V6003"
## [6004] "V6004" "V6005" "V6006" "V6007" "V6008" "V6009" "V6010" "V6011" "V6012"
## [6013] "V6013" "V6014" "V6015" "V6016" "V6017" "V6018" "V6019" "V6020" "V6021"
## [6022] "V6022" "V6023" "V6024" "V6025" "V6026" "V6027" "V6028" "V6029" "V6030"
## [6031] "V6031" "V6032" "V6033" "V6034" "V6035" "V6036" "V6037" "V6038" "V6039"
## [6040] "V6040" "V6041" "V6042" "V6043" "V6044" "V6045" "V6046" "V6047" "V6048"
## [6049] "V6049" "V6050" "V6051" "V6052" "V6053" "V6054" "V6055" "V6056" "V6057"
## [6058] "V6058" "V6059" "V6060" "V6061" "V6062" "V6063" "V6064" "V6065" "V6066"
## [6067] "V6067" "V6068" "V6069" "V6070" "V6071" "V6072" "V6073" "V6074" "V6075"
## [6076] "V6076" "V6077" "V6078" "V6079" "V6080" "V6081" "V6082" "V6083" "V6084"
## [6085] "V6085" "V6086" "V6087" "V6088" "V6089" "V6090" "V6091" "V6092" "V6093"
## [6094] "V6094" "V6095" "V6096" "V6097" "V6098" "V6099" "V6100" "V6101" "V6102"
## [6103] "V6103" "V6104" "V6105" "V6106" "V6107" "V6108" "V6109" "V6110" "V6111"
## [6112] "V6112" "V6113" "V6114" "V6115" "V6116" "V6117" "V6118" "V6119" "V6120"
## [6121] "V6121" "V6122" "V6123" "V6124" "V6125" "V6126" "V6127" "V6128" "V6129"
## [6130] "V6130" "V6131" "V6132" "V6133" "V6134" "V6135" "V6136" "V6137" "V6138"
## [6139] "V6139" "V6140" "V6141" "V6142" "V6143" "V6144" "V6145" "V6146" "V6147"
## [6148] "V6148" "V6149" "V6150" "V6151" "V6152" "V6153" "V6154" "V6155" "V6156"
## [6157] "V6157" "V6158" "V6159" "V6160" "V6161" "V6162" "V6163" "V6164" "V6165"
## [6166] "V6166" "V6167" "V6168" "V6169" "V6170" "V6171" "V6172" "V6173" "V6174"
## [6175] "V6175" "V6176" "V6177" "V6178" "V6179" "V6180" "V6181" "V6182" "V6183"
## [6184] "V6184" "V6185" "V6186" "V6187" "V6188" "V6189" "V6190" "V6191" "V6192"
## [6193] "V6193" "V6194" "V6195" "V6196" "V6197" "V6198" "V6199" "V6200" "V6201"
## [6202] "V6202" "V6203" "V6204" "V6205" "V6206" "V6207" "V6208" "V6209" "V6210"
## [6211] "V6211" "V6212" "V6213" "V6214" "V6215" "V6216" "V6217" "V6218" "V6219"
## [6220] "V6220" "V6221" "V6222" "V6223" "V6224" "V6225" "V6226" "V6227" "V6228"
## [6229] "V6229" "V6230" "V6231" "V6232" "V6233" "V6234" "V6235" "V6236" "V6237"
## [6238] "V6238" "V6239" "V6240" "V6241" "V6242" "V6243" "V6244" "V6245" "V6246"
## [6247] "V6247" "V6248" "V6249" "V6250" "V6251" "V6252" "V6253" "V6254" "V6255"
## [6256] "V6256" "V6257" "V6258" "V6259" "V6260" "V6261" "V6262" "V6263" "V6264"
## [6265] "V6265" "V6266" "V6267" "V6268" "V6269" "V6270" "V6271" "V6272" "V6273"
## [6274] "V6274" "V6275" "V6276" "V6277" "V6278" "V6279" "V6280" "V6281" "V6282"
## [6283] "V6283" "V6284" "V6285" "V6286" "V6287" "V6288" "V6289" "V6290" "V6291"
## [6292] "V6292" "V6293" "V6294" "V6295" "V6296" "V6297" "V6298" "V6299" "V6300"
## [6301] "V6301" "V6302" "V6303" "V6304" "V6305" "V6306" "V6307" "V6308" "V6309"
## [6310] "V6310" "V6311" "V6312" "V6313" "V6314" "V6315" "V6316" "V6317" "V6318"
## [6319] "V6319" "V6320" "V6321" "V6322" "V6323" "V6324" "V6325" "V6326" "V6327"
## [6328] "V6328" "V6329" "V6330" "V6331" "V6332" "V6333" "V6334" "V6335" "V6336"
## [6337] "V6337" "V6338" "V6339" "V6340" "V6341" "V6342" "V6343" "V6344" "V6345"
## [6346] "V6346" "V6347" "V6348" "V6349" "V6350" "V6351" "V6352" "V6353" "V6354"
## [6355] "V6355" "V6356" "V6357" "V6358" "V6359" "V6360" "V6361" "V6362" "V6363"
## [6364] "V6364" "V6365" "V6366" "V6367" "V6368" "V6369" "V6370" "V6371" "V6372"
## [6373] "V6373" "V6374" "V6375" "V6376" "V6377" "V6378" "V6379" "V6380" "V6381"
## [6382] "V6382" "V6383" "V6384" "V6385" "V6386" "V6387" "V6388" "V6389" "V6390"
## [6391] "V6391" "V6392" "V6393" "V6394" "V6395" "V6396" "V6397" "V6398" "V6399"
## [6400] "V6400" "V6401" "V6402" "V6403" "V6404" "V6405" "V6406" "V6407" "V6408"
## [6409] "V6409" "V6410" "V6411" "V6412" "V6413" "V6414" "V6415" "V6416" "V6417"
## [6418] "V6418" "V6419" "V6420" "V6421" "V6422" "V6423" "V6424" "V6425" "V6426"
## [6427] "V6427" "V6428" "V6429" "V6430" "V6431" "V6432" "V6433" "V6434" "V6435"
## [6436] "V6436" "V6437" "V6438" "V6439" "V6440" "V6441" "V6442" "V6443" "V6444"
## [6445] "V6445" "V6446" "V6447" "V6448" "V6449" "V6450" "V6451" "V6452" "V6453"
## [6454] "V6454" "V6455" "V6456" "V6457" "V6458" "V6459" "V6460" "V6461" "V6462"
## [6463] "V6463" "V6464" "V6465" "V6466" "V6467" "V6468" "V6469" "V6470" "V6471"
## [6472] "V6472" "V6473" "V6474" "V6475" "V6476" "V6477" "V6478" "V6479" "V6480"
## [6481] "V6481" "V6482" "V6483" "V6484" "V6485" "V6486" "V6487" "V6488" "V6489"
## [6490] "V6490" "V6491" "V6492" "V6493" "V6494" "V6495" "V6496" "V6497" "V6498"
## [6499] "V6499" "V6500" "V6501" "V6502" "V6503" "V6504" "V6505" "V6506" "V6507"
## [6508] "V6508" "V6509" "V6510" "V6511" "V6512" "V6513" "V6514" "V6515" "V6516"
## [6517] "V6517" "V6518" "V6519" "V6520" "V6521" "V6522" "V6523" "V6524" "V6525"
## [6526] "V6526" "V6527" "V6528" "V6529" "V6530" "V6531" "V6532" "V6533" "V6534"
## [6535] "V6535" "V6536" "V6537" "V6538" "V6539" "V6540" "V6541" "V6542" "V6543"
## [6544] "V6544" "V6545" "V6546" "V6547" "V6548" "V6549" "V6550" "V6551" "V6552"
## [6553] "V6553" "V6554" "V6555" "V6556" "V6557" "V6558" "V6559" "V6560" "V6561"
## [6562] "V6562" "V6563" "V6564" "V6565" "V6566" "V6567" "V6568" "V6569" "V6570"
## [6571] "V6571" "V6572" "V6573" "V6574" "V6575" "V6576" "V6577" "V6578" "V6579"
## [6580] "V6580" "V6581" "V6582" "V6583" "V6584" "V6585" "V6586" "V6587" "V6588"
## [6589] "V6589" "V6590" "V6591" "V6592" "V6593" "V6594" "V6595" "V6596" "V6597"
## [6598] "V6598" "V6599" "V6600" "V6601" "V6602" "V6603" "V6604" "V6605" "V6606"
## [6607] "V6607" "V6608" "V6609" "V6610" "V6611" "V6612" "V6613" "V6614" "V6615"
## [6616] "V6616" "V6617" "V6618" "V6619" "V6620" "V6621" "V6622" "V6623" "V6624"
## [6625] "V6625" "V6626" "V6627" "V6628" "V6629" "V6630" "V6631" "V6632" "V6633"
## [6634] "V6634" "V6635" "V6636" "V6637" "V6638" "V6639" "V6640" "V6641" "V6642"
## [6643] "V6643" "V6644" "V6645" "V6646" "V6647" "V6648" "V6649" "V6650" "V6651"
## [6652] "V6652" "V6653" "V6654" "V6655" "V6656" "V6657" "V6658" "V6659" "V6660"
## [6661] "V6661" "V6662" "V6663" "V6664" "V6665" "V6666" "V6667" "V6668" "V6669"
## [6670] "V6670" "V6671" "V6672" "V6673" "V6674" "V6675" "V6676" "V6677" "V6678"
## [6679] "V6679" "V6680" "V6681" "V6682" "V6683" "V6684" "V6685" "V6686" "V6687"
## [6688] "V6688" "V6689" "V6690" "V6691" "V6692" "V6693" "V6694" "V6695" "V6696"
## [6697] "V6697" "V6698" "V6699" "V6700" "V6701" "V6702" "V6703" "V6704" "V6705"
## [6706] "V6706" "V6707" "V6708" "V6709" "V6710" "V6711" "V6712" "V6713" "V6714"
## [6715] "V6715" "V6716" "V6717" "V6718" "V6719" "V6720" "V6721" "V6722" "V6723"
## [6724] "V6724" "V6725" "V6726" "V6727" "V6728" "V6729" "V6730" "V6731" "V6732"
## [6733] "V6733" "V6734" "V6735" "V6736" "V6737" "V6738" "V6739" "V6740" "V6741"
## [6742] "V6742" "V6743" "V6744" "V6745" "V6746" "V6747" "V6748" "V6749" "V6750"
## [6751] "V6751" "V6752" "V6753" "V6754" "V6755" "V6756" "V6757" "V6758" "V6759"
## [6760] "V6760" "V6761" "V6762" "V6763" "V6764" "V6765" "V6766" "V6767" "V6768"
## [6769] "V6769" "V6770" "V6771" "V6772" "V6773" "V6774" "V6775" "V6776" "V6777"
## [6778] "V6778" "V6779" "V6780" "V6781" "V6782" "V6783" "V6784" "V6785" "V6786"
## [6787] "V6787" "V6788" "V6789" "V6790" "V6791" "V6792" "V6793" "V6794" "V6795"
## [6796] "V6796" "V6797" "V6798" "V6799" "V6800" "V6801" "V6802" "V6803" "V6804"
## [6805] "V6805" "V6806" "V6807" "V6808" "V6809" "V6810" "V6811" "V6812" "V6813"
## [6814] "V6814" "V6815" "V6816" "V6817" "V6818" "V6819" "V6820" "V6821" "V6822"
## [6823] "V6823" "V6824" "V6825" "V6826" "V6827" "V6828" "V6829" "V6830" "V6831"
## [6832] "V6832" "V6833" "V6834" "V6835" "V6836" "V6837" "V6838" "V6839" "V6840"
## [6841] "V6841" "V6842" "V6843" "V6844" "V6845" "V6846" "V6847" "V6848" "V6849"
## [6850] "V6850" "V6851" "V6852" "V6853" "V6854" "V6855" "V6856" "V6857" "V6858"
## [6859] "V6859" "V6860" "V6861" "V6862" "V6863" "V6864" "V6865" "V6866" "V6867"
## [6868] "V6868" "V6869" "V6870" "V6871" "V6872" "V6873" "V6874" "V6875" "V6876"
## [6877] "V6877" "V6878" "V6879" "V6880" "V6881" "V6882" "V6883" "V6884" "V6885"
## [6886] "V6886" "V6887" "V6888" "V6889" "V6890" "V6891" "V6892" "V6893" "V6894"
## [6895] "V6895" "V6896" "V6897" "V6898" "V6899" "V6900" "V6901" "V6902" "V6903"
## [6904] "V6904" "V6905" "V6906" "V6907" "V6908" "V6909" "V6910" "V6911" "V6912"
## [6913] "V6913" "V6914" "V6915" "V6916" "V6917" "V6918" "V6919" "V6920" "V6921"
## [6922] "V6922" "V6923" "V6924" "V6925" "V6926" "V6927" "V6928" "V6929" "V6930"
## [6931] "V6931" "V6932" "V6933" "V6934" "V6935" "V6936" "V6937" "V6938" "V6939"
## [6940] "V6940" "V6941" "V6942" "V6943" "V6944" "V6945" "V6946" "V6947" "V6948"
## [6949] "V6949" "V6950" "V6951" "V6952" "V6953" "V6954" "V6955" "V6956" "V6957"
## [6958] "V6958" "V6959" "V6960" "V6961" "V6962" "V6963" "V6964" "V6965" "V6966"
## [6967] "V6967" "V6968" "V6969" "V6970" "V6971" "V6972" "V6973" "V6974" "V6975"
## [6976] "V6976" "V6977" "V6978" "V6979" "V6980" "V6981" "V6982" "V6983" "V6984"
## [6985] "V6985" "V6986" "V6987" "V6988" "V6989" "V6990" "V6991" "V6992" "V6993"
## [6994] "V6994" "V6995" "V6996" "V6997" "V6998" "V6999" "V7000" "V7001" "V7002"
## [7003] "V7003" "V7004" "V7005" "V7006" "V7007" "V7008" "V7009" "V7010" "V7011"
## [7012] "V7012" "V7013" "V7014" "V7015" "V7016" "V7017" "V7018" "V7019" "V7020"
## [7021] "V7021" "V7022" "V7023" "V7024" "V7025" "V7026" "V7027" "V7028" "V7029"
## [7030] "V7030" "V7031" "V7032" "V7033" "V7034" "V7035" "V7036" "V7037" "V7038"
## [7039] "V7039" "V7040" "V7041" "V7042" "V7043" "V7044" "V7045" "V7046" "V7047"
## [7048] "V7048" "V7049" "V7050" "V7051" "V7052" "V7053" "V7054" "V7055" "V7056"
## [7057] "V7057" "V7058" "V7059" "V7060" "V7061" "V7062" "V7063" "V7064" "V7065"
## [7066] "V7066" "V7067" "V7068" "V7069" "V7070" "V7071" "V7072" "V7073" "V7074"
## [7075] "V7075" "V7076" "V7077" "V7078" "V7079" "V7080" "V7081" "V7082" "V7083"
## [7084] "V7084" "V7085" "V7086" "V7087" "V7088" "V7089" "V7090" "V7091" "V7092"
## [7093] "V7093" "V7094" "V7095" "V7096" "V7097" "V7098" "V7099" "V7100" "V7101"
## [7102] "V7102" "V7103" "V7104" "V7105" "V7106" "V7107" "V7108" "V7109" "V7110"
## [7111] "V7111" "V7112" "V7113" "V7114" "V7115" "V7116" "V7117" "V7118" "V7119"
## [7120] "V7120" "V7121" "V7122" "V7123" "V7124" "V7125" "V7126" "V7127" "V7128"
## [7129] "V7129" "V7130" "V7131" "V7132" "V7133" "V7134" "V7135" "V7136" "V7137"
## [7138] "V7138" "V7139" "V7140" "V7141" "V7142" "V7143" "V7144" "V7145" "V7146"
## [7147] "V7147" "V7148" "V7149" "V7150" "V7151" "V7152" "V7153" "V7154" "V7155"
## [7156] "V7156" "V7157" "V7158" "V7159" "V7160" "V7161" "V7162" "V7163" "V7164"
## [7165] "V7165" "V7166" "V7167" "V7168" "V7169" "V7170" "V7171" "V7172" "V7173"
## [7174] "V7174" "V7175" "V7176" "V7177" "V7178" "V7179" "V7180" "V7181" "V7182"
## [7183] "V7183" "V7184" "V7185" "V7186" "V7187" "V7188" "V7189" "V7190" "V7191"
## [7192] "V7192" "V7193" "V7194" "V7195" "V7196" "V7197" "V7198" "V7199" "V7200"
## [7201] "V7201" "V7202" "V7203" "V7204" "V7205" "V7206" "V7207" "V7208" "V7209"
## [7210] "V7210" "V7211" "V7212" "V7213" "V7214" "V7215" "V7216" "V7217" "V7218"
## [7219] "V7219" "V7220" "V7221" "V7222" "V7223" "V7224" "V7225" "V7226" "V7227"
## [7228] "V7228" "V7229" "V7230" "V7231" "V7232" "V7233" "V7234" "V7235" "V7236"
## [7237] "V7237" "V7238" "V7239" "V7240" "V7241" "V7242" "V7243" "V7244" "V7245"
## [7246] "V7246" "V7247" "V7248" "V7249" "V7250" "V7251" "V7252" "V7253" "V7254"
## [7255] "V7255" "V7256" "V7257" "V7258" "V7259" "V7260" "V7261" "V7262" "V7263"
## [7264] "V7264" "V7265" "V7266" "V7267" "V7268" "V7269" "V7270" "V7271" "V7272"
## [7273] "V7273" "V7274" "V7275" "V7276" "V7277" "V7278" "V7279" "V7280" "V7281"
## [7282] "V7282" "V7283" "V7284" "V7285" "V7286" "V7287" "V7288" "V7289" "V7290"
## [7291] "V7291" "V7292" "V7293" "V7294" "V7295" "V7296" "V7297" "V7298" "V7299"
## [7300] "V7300" "V7301" "V7302" "V7303" "V7304" "V7305" "V7306" "V7307" "V7308"
## [7309] "V7309" "V7310" "V7311" "V7312" "V7313" "V7314" "V7315" "V7316" "V7317"
## [7318] "V7318" "V7319" "V7320" "V7321" "V7322" "V7323" "V7324" "V7325" "V7326"
## [7327] "V7327" "V7328" "V7329" "V7330" "V7331" "V7332" "V7333" "V7334" "V7335"
## [7336] "V7336" "V7337" "V7338" "V7339" "V7340" "V7341" "V7342" "V7343" "V7344"
## [7345] "V7345" "V7346" "V7347" "V7348" "V7349" "V7350" "V7351" "V7352" "V7353"
## [7354] "V7354" "V7355" "V7356" "V7357" "V7358" "V7359" "V7360" "V7361" "V7362"
## [7363] "V7363" "V7364" "V7365" "V7366" "V7367" "V7368" "V7369" "V7370" "V7371"
## [7372] "V7372" "V7373" "V7374" "V7375" "V7376" "V7377" "V7378" "V7379" "V7380"
## [7381] "V7381" "V7382" "V7383" "V7384" "V7385" "V7386" "V7387" "V7388" "V7389"
## [7390] "V7390" "V7391" "V7392" "V7393" "V7394" "V7395" "V7396" "V7397" "V7398"
## [7399] "V7399" "V7400" "V7401" "V7402" "V7403" "V7404" "V7405" "V7406" "V7407"
## [7408] "V7408" "V7409" "V7410" "V7411" "V7412" "V7413" "V7414" "V7415" "V7416"
## [7417] "V7417" "V7418" "V7419" "V7420" "V7421" "V7422" "V7423" "V7424" "V7425"
## [7426] "V7426" "V7427" "V7428" "V7429" "V7430" "V7431" "V7432" "V7433" "V7434"
## [7435] "V7435" "V7436" "V7437" "V7438" "V7439" "V7440" "V7441" "V7442" "V7443"
## [7444] "V7444" "V7445" "V7446" "V7447" "V7448" "V7449" "V7450" "V7451" "V7452"
## [7453] "V7453" "V7454" "V7455" "V7456" "V7457" "V7458" "V7459" "V7460" "V7461"
## [7462] "V7462" "V7463" "V7464" "V7465" "V7466" "V7467" "V7468" "V7469" "V7470"
## [7471] "V7471" "V7472" "V7473" "V7474" "V7475" "V7476" "V7477" "V7478" "V7479"
## [7480] "V7480" "V7481" "V7482" "V7483" "V7484" "V7485" "V7486" "V7487" "V7488"
## [7489] "V7489" "V7490" "V7491" "V7492" "V7493" "V7494" "V7495" "V7496" "V7497"
## [7498] "V7498" "V7499" "V7500" "V7501" "V7502" "V7503" "V7504" "V7505" "V7506"
## [7507] "V7507" "V7508" "V7509" "V7510" "V7511" "V7512" "V7513" "V7514" "V7515"
## [7516] "V7516" "V7517" "V7518" "V7519" "V7520" "V7521" "V7522" "V7523" "V7524"
## [7525] "V7525" "V7526" "V7527" "V7528" "V7529" "V7530" "V7531" "V7532" "V7533"
## [7534] "V7534" "V7535" "V7536" "V7537" "V7538" "V7539" "V7540" "V7541" "V7542"
## [7543] "V7543" "V7544" "V7545" "V7546" "V7547" "V7548" "V7549" "V7550" "V7551"
## [7552] "V7552" "V7553" "V7554" "V7555" "V7556" "V7557" "V7558" "V7559" "V7560"
## [7561] "V7561" "V7562" "V7563" "V7564" "V7565" "V7566" "V7567" "V7568" "V7569"
## [7570] "V7570" "V7571" "V7572" "V7573" "V7574" "V7575" "V7576" "V7577" "V7578"
## [7579] "V7579" "V7580" "V7581" "V7582" "V7583" "V7584" "V7585" "V7586" "V7587"
## [7588] "V7588" "V7589" "V7590" "V7591" "V7592" "V7593" "V7594" "V7595" "V7596"
## [7597] "V7597" "V7598" "V7599" "V7600" "V7601" "V7602" "V7603" "V7604" "V7605"
## [7606] "V7606" "V7607" "V7608" "V7609" "V7610" "V7611" "V7612" "V7613" "V7614"
## [7615] "V7615" "V7616" "V7617" "V7618" "V7619" "V7620" "V7621" "V7622" "V7623"
## [7624] "V7624" "V7625" "V7626" "V7627" "V7628" "V7629" "V7630" "V7631" "V7632"
## [7633] "V7633" "V7634" "V7635" "V7636" "V7637" "V7638" "V7639" "V7640" "V7641"
## [7642] "V7642" "V7643" "V7644" "V7645" "V7646" "V7647" "V7648" "V7649" "V7650"
## [7651] "V7651" "V7652" "V7653" "V7654" "V7655" "V7656" "V7657" "V7658" "V7659"
## [7660] "V7660" "V7661" "V7662" "V7663" "V7664" "V7665" "V7666" "V7667" "V7668"
## [7669] "V7669" "V7670" "V7671" "V7672" "V7673" "V7674" "V7675" "V7676" "V7677"
## [7678] "V7678" "V7679" "V7680" "V7681" "V7682" "V7683" "V7684" "V7685" "V7686"
## [7687] "V7687" "V7688" "V7689" "V7690" "V7691" "V7692" "V7693" "V7694" "V7695"
## [7696] "V7696" "V7697" "V7698" "V7699" "V7700" "V7701" "V7702" "V7703" "V7704"
## [7705] "V7705" "V7706" "V7707" "V7708" "V7709" "V7710" "V7711" "V7712" "V7713"
## [7714] "V7714" "V7715" "V7716" "V7717" "V7718" "V7719" "V7720" "V7721" "V7722"
## [7723] "V7723" "V7724" "V7725" "V7726" "V7727" "V7728" "V7729" "V7730" "V7731"
## [7732] "V7732" "V7733" "V7734" "V7735" "V7736" "V7737" "V7738" "V7739" "V7740"
## [7741] "V7741" "V7742" "V7743" "V7744" "V7745" "V7746" "V7747" "V7748" "V7749"
## [7750] "V7750" "V7751" "V7752" "V7753" "V7754" "V7755" "V7756" "V7757" "V7758"
## [7759] "V7759" "V7760" "V7761" "V7762" "V7763" "V7764" "V7765" "V7766" "V7767"
## [7768] "V7768" "V7769" "V7770" "V7771" "V7772" "V7773" "V7774" "V7775" "V7776"
## [7777] "V7777" "V7778" "V7779" "V7780" "V7781" "V7782" "V7783" "V7784" "V7785"
## [7786] "V7786" "V7787" "V7788" "V7789" "V7790" "V7791" "V7792" "V7793" "V7794"
## [7795] "V7795" "V7796" "V7797" "V7798" "V7799" "V7800" "V7801" "V7802" "V7803"
## [7804] "V7804" "V7805" "V7806" "V7807" "V7808" "V7809" "V7810" "V7811" "V7812"
## [7813] "V7813" "V7814" "V7815" "V7816" "V7817" "V7818" "V7819" "V7820" "V7821"
## [7822] "V7822" "V7823" "V7824" "V7825" "V7826" "V7827" "V7828" "V7829" "V7830"
## [7831] "V7831" "V7832" "V7833" "V7834" "V7835" "V7836" "V7837" "V7838" "V7839"
## [7840] "V7840" "V7841" "V7842" "V7843" "V7844" "V7845" "V7846" "V7847" "V7848"
## [7849] "V7849" "V7850" "V7851" "V7852" "V7853" "V7854" "V7855" "V7856" "V7857"
## [7858] "V7858" "V7859" "V7860" "V7861" "V7862" "V7863" "V7864" "V7865" "V7866"
## [7867] "V7867" "V7868" "V7869" "V7870" "V7871" "V7872" "V7873" "V7874" "V7875"
## [7876] "V7876" "V7877" "V7878" "V7879" "V7880" "V7881" "V7882" "V7883" "V7884"
## [7885] "V7885" "V7886" "V7887" "V7888" "V7889" "V7890" "V7891" "V7892" "V7893"
## [7894] "V7894" "V7895" "V7896" "V7897" "V7898" "V7899" "V7900" "V7901" "V7902"
## [7903] "V7903" "V7904" "V7905" "V7906" "V7907" "V7908" "V7909" "V7910" "V7911"
## [7912] "V7912" "V7913" "V7914" "V7915" "V7916" "V7917" "V7918" "V7919" "V7920"
## [7921] "V7921" "V7922" "V7923" "V7924" "V7925" "V7926" "V7927" "V7928" "V7929"
## [7930] "V7930" "V7931" "V7932" "V7933" "V7934" "V7935" "V7936" "V7937" "V7938"
## [7939] "V7939" "V7940" "V7941" "V7942" "V7943" "V7944" "V7945" "V7946" "V7947"
## [7948] "V7948" "V7949" "V7950" "V7951" "V7952" "V7953" "V7954" "V7955" "V7956"
## [7957] "V7957" "V7958" "V7959" "V7960" "V7961" "V7962" "V7963" "V7964" "V7965"
## [7966] "V7966" "V7967" "V7968" "V7969" "V7970" "V7971" "V7972" "V7973" "V7974"
## [7975] "V7975" "V7976" "V7977" "V7978" "V7979" "V7980" "V7981" "V7982" "V7983"
## [7984] "V7984" "V7985" "V7986" "V7987" "V7988" "V7989" "V7990" "V7991" "V7992"
## [7993] "V7993" "V7994" "V7995" "V7996" "V7997" "V7998" "V7999" "V8000" "V8001"
## [8002] "V8002" "V8003" "V8004" "V8005" "V8006" "V8007" "V8008" "V8009" "V8010"
## [8011] "V8011" "V8012" "V8013" "V8014" "V8015" "V8016" "V8017" "V8018" "V8019"
## [8020] "V8020" "V8021" "V8022" "V8023" "V8024" "V8025" "V8026" "V8027" "V8028"
## [8029] "V8029" "V8030" "V8031" "V8032" "V8033" "V8034" "V8035" "V8036" "V8037"
## [8038] "V8038" "V8039" "V8040" "V8041" "V8042" "V8043" "V8044" "V8045" "V8046"
## [8047] "V8047" "V8048" "V8049" "V8050" "V8051" "V8052" "V8053" "V8054" "V8055"
## [8056] "V8056" "V8057" "V8058" "V8059" "V8060" "V8061" "V8062" "V8063" "V8064"
## [8065] "V8065" "V8066" "V8067" "V8068" "V8069" "V8070" "V8071" "V8072" "V8073"
## [8074] "V8074" "V8075" "V8076" "V8077" "V8078" "V8079" "V8080" "V8081" "V8082"
## [8083] "V8083" "V8084" "V8085" "V8086" "V8087" "V8088" "V8089" "V8090" "V8091"
## [8092] "V8092" "V8093" "V8094" "V8095" "V8096" "V8097" "V8098" "V8099" "V8100"
## [8101] "V8101" "V8102" "V8103" "V8104" "V8105" "V8106" "V8107" "V8108" "V8109"
## [8110] "V8110" "V8111" "V8112" "V8113" "V8114" "V8115" "V8116" "V8117" "V8118"
## [8119] "V8119" "V8120" "V8121" "V8122" "V8123" "V8124" "V8125" "V8126" "V8127"
## [8128] "V8128" "V8129" "V8130" "V8131" "V8132" "V8133" "V8134" "V8135" "V8136"
## [8137] "V8137" "V8138" "V8139" "V8140" "V8141" "V8142" "V8143" "V8144" "V8145"
## [8146] "V8146" "V8147" "V8148" "V8149" "V8150" "V8151" "V8152" "V8153" "V8154"
## [8155] "V8155" "V8156" "V8157" "V8158" "V8159" "V8160" "V8161" "V8162" "V8163"
## [8164] "V8164" "V8165" "V8166" "V8167" "V8168" "V8169" "V8170" "V8171" "V8172"
## [8173] "V8173" "V8174" "V8175" "V8176" "V8177" "V8178" "V8179" "V8180" "V8181"
## [8182] "V8182" "V8183" "V8184" "V8185" "V8186" "V8187" "V8188" "V8189" "V8190"
## [8191] "V8191" "V8192" "V8193" "V8194" "V8195" "V8196" "V8197" "V8198" "V8199"
## [8200] "V8200" "V8201" "V8202" "V8203" "V8204" "V8205" "V8206" "V8207" "V8208"
## [8209] "V8209" "V8210" "V8211" "V8212" "V8213" "V8214" "V8215" "V8216" "V8217"
## [8218] "V8218" "V8219" "V8220" "V8221" "V8222" "V8223" "V8224" "V8225" "V8226"
## [8227] "V8227" "V8228" "V8229" "V8230" "V8231" "V8232" "V8233" "V8234" "V8235"
## [8236] "V8236" "V8237" "V8238" "V8239" "V8240" "V8241" "V8242" "V8243" "V8244"
## [8245] "V8245" "V8246" "V8247" "V8248" "V8249" "V8250" "V8251" "V8252" "V8253"
## [8254] "V8254" "V8255" "V8256" "V8257" "V8258" "V8259" "V8260" "V8261" "V8262"
## [8263] "V8263" "V8264" "V8265" "V8266" "V8267" "V8268" "V8269" "V8270" "V8271"
## [8272] "V8272" "V8273" "V8274" "V8275" "V8276" "V8277" "V8278" "V8279" "V8280"
## [8281] "V8281" "V8282" "V8283" "V8284" "V8285" "V8286" "V8287" "V8288" "V8289"
## [8290] "V8290" "V8291" "V8292" "V8293" "V8294" "V8295" "V8296" "V8297" "V8298"
## [8299] "V8299" "V8300" "V8301" "V8302" "V8303" "V8304" "V8305" "V8306" "V8307"
## [8308] "V8308" "V8309" "V8310" "V8311" "V8312" "V8313" "V8314" "V8315" "V8316"
## [8317] "V8317" "V8318" "V8319" "V8320" "V8321" "V8322" "V8323" "V8324" "V8325"
## [8326] "V8326" "V8327" "V8328" "V8329" "V8330" "V8331" "V8332" "V8333" "V8334"
## [8335] "V8335" "V8336" "V8337" "V8338" "V8339" "V8340" "V8341" "V8342" "V8343"
## [8344] "V8344" "V8345" "V8346" "V8347" "V8348" "V8349" "V8350" "V8351" "V8352"
## [8353] "V8353" "V8354" "V8355" "V8356" "V8357" "V8358" "V8359" "V8360" "V8361"
## [8362] "V8362" "V8363" "V8364" "V8365" "V8366" "V8367" "V8368" "V8369" "V8370"
## [8371] "V8371" "V8372" "V8373" "V8374" "V8375" "V8376" "V8377" "V8378" "V8379"
## [8380] "V8380" "V8381" "V8382" "V8383" "V8384" "V8385" "V8386" "V8387" "V8388"
## [8389] "V8389" "V8390" "V8391" "V8392" "V8393" "V8394" "V8395" "V8396" "V8397"
## [8398] "V8398" "V8399" "V8400"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200# las filas son el número de puntos de evaluación de la función y las columnas el número de curvas
# Suavizamiento spline
# Creación de la base de funciones
npuntos <- nrow(imagenesEntxeydf)
base_bspline <- create.bspline.basis(c(1, npuntos), 150)
plot(base_bspline)
summary(base_bspline)##
## Basis object:
##
## Type: bspline
##
## Range: 1 to 200
##
## Number of basis functions: 150# Selección de lambda
gcv <- NULL
lambda_list <- seq(-2, 4, length.out = 30)
# estos son las potencias de 10 de variación del lambda
# inicia con valores muy cercanos a cero 10^-2 hasta 10^4
matrix_data <- as.matrix(imagenesEntxeydf)
for (lambda in lambda_list) {
lambda <- 10**lambda
param_lambda <- fdPar(base_bspline,
2,
lambda)
fd_smoothEnt <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
gcv <- c(gcv, sum(fd_smoothEnt$gcv))
}
# Se esta tomando como criterio total la suma de las VC de todas las curvas
plotgcv <-
data.frame(log_lambda = lambda_list,
gcv = gcv) %>% ggplot(aes(x = log_lambda, y = gcv)) +
geom_line(color = "purple", linetype = "dashed") +
theme_light() +
scale_x_continuous(limits = c(0, 4))
plotly::ggplotly(plotgcv)selected_lamdba <- 10**lambda_list[which.min(gcv)]
print(selected_lamdba)## [1] 0.02592944print(log(selected_lamdba, 10))## [1] -1.586207# entrega el valor de lambda donde se minimiza la suma de VC y log de lambda
# Suavizamiento con el lambda elegido
param_lambda <- fdPar(base_bspline,
2,
lambda = selected_lamdba)
fd_smoothEnt <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
plot(fd_smoothEnt, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(fd_smoothEnt$fd[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
fd_fittedEnt <- fitted(fd_smoothEnt)
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedEnt[,1],col="2")
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))3. Cargar los datos funcionales de las imagenes de ensayo y crear objeto fd con dichas curvas en base bspline
imagenesEnsxey <- NULL
for (j in 1:3) {
imagenductil <- read.bitmap(paste("DuctilEns", j,".jpg", sep = "")) # crea una matriz de x filas y columanas y z=3 canales
imagenductil <- imagenductil [,,1] # Deja un solo canal
imagenductilt <- t(imagenductil)
imagenductilxey <- rbind(imagenductil,imagenductilt)
imagenductilxey <- t(imagenductilxey)
imagenesEnsxey <- cbind(imagenesEnsxey,imagenductilxey)}
attributes(imagenesEnsxey)## $dim
## [1] 200 1200for (j in 1:3) {
imagenfragil <- read.bitmap(paste("FragilEns", j,".jpg", sep = "")) # crea una matriz de x filas y columanas y z=3 canales
imagenfragil <- imagenfragil [,,1] # Deja un solo canal
imagenfragilt <- t(imagenfragil)
imagenfragilxey <- rbind(imagenfragil,imagenfragilt)
imagenfragilxey <- t(imagenfragilxey)
imagenesEnsxey <- cbind(imagenesEnsxey,imagenfragilxey)}
attributes(imagenesEnsxey)## $dim
## [1] 200 2400for (j in 1:3) {
imagenfatiga <- read.bitmap(paste("FatigaEns", j,".jpg", sep = "")) # crea una matriz de x filas y columanas y z=3 canales
imagenfatiga <- imagenfatiga [,,1] # Deja un solo canal
imagenfatigat <- t(imagenfatiga)
imagenfatigaxey <- rbind(imagenfatiga,imagenfatigat)
imagenfatigaxey <- t(imagenfatigaxey)
imagenesEnsxey <- cbind(imagenesEnsxey,imagenfatigaxey)}
attributes(imagenesEnsxey)## $dim
## [1] 200 3600# Convertir en Data Frame Ensayo
imagenesEnsxeydf <- as.data.frame (imagenesEnsxey)
attributes(imagenesEnsxeydf)## $names
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9"
## [10] "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18"
## [19] "V19" "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27"
## [28] "V28" "V29" "V30" "V31" "V32" "V33" "V34" "V35" "V36"
## [37] "V37" "V38" "V39" "V40" "V41" "V42" "V43" "V44" "V45"
## [46] "V46" "V47" "V48" "V49" "V50" "V51" "V52" "V53" "V54"
## [55] "V55" "V56" "V57" "V58" "V59" "V60" "V61" "V62" "V63"
## [64] "V64" "V65" "V66" "V67" "V68" "V69" "V70" "V71" "V72"
## [73] "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80" "V81"
## [82] "V82" "V83" "V84" "V85" "V86" "V87" "V88" "V89" "V90"
## [91] "V91" "V92" "V93" "V94" "V95" "V96" "V97" "V98" "V99"
## [100] "V100" "V101" "V102" "V103" "V104" "V105" "V106" "V107" "V108"
## [109] "V109" "V110" "V111" "V112" "V113" "V114" "V115" "V116" "V117"
## [118] "V118" "V119" "V120" "V121" "V122" "V123" "V124" "V125" "V126"
## [127] "V127" "V128" "V129" "V130" "V131" "V132" "V133" "V134" "V135"
## [136] "V136" "V137" "V138" "V139" "V140" "V141" "V142" "V143" "V144"
## [145] "V145" "V146" "V147" "V148" "V149" "V150" "V151" "V152" "V153"
## [154] "V154" "V155" "V156" "V157" "V158" "V159" "V160" "V161" "V162"
## [163] "V163" "V164" "V165" "V166" "V167" "V168" "V169" "V170" "V171"
## [172] "V172" "V173" "V174" "V175" "V176" "V177" "V178" "V179" "V180"
## [181] "V181" "V182" "V183" "V184" "V185" "V186" "V187" "V188" "V189"
## [190] "V190" "V191" "V192" "V193" "V194" "V195" "V196" "V197" "V198"
## [199] "V199" "V200" "V201" "V202" "V203" "V204" "V205" "V206" "V207"
## [208] "V208" "V209" "V210" "V211" "V212" "V213" "V214" "V215" "V216"
## [217] "V217" "V218" "V219" "V220" "V221" "V222" "V223" "V224" "V225"
## [226] "V226" "V227" "V228" "V229" "V230" "V231" "V232" "V233" "V234"
## [235] "V235" "V236" "V237" "V238" "V239" "V240" "V241" "V242" "V243"
## [244] "V244" "V245" "V246" "V247" "V248" "V249" "V250" "V251" "V252"
## [253] "V253" "V254" "V255" "V256" "V257" "V258" "V259" "V260" "V261"
## [262] "V262" "V263" "V264" "V265" "V266" "V267" "V268" "V269" "V270"
## [271] "V271" "V272" "V273" "V274" "V275" "V276" "V277" "V278" "V279"
## [280] "V280" "V281" "V282" "V283" "V284" "V285" "V286" "V287" "V288"
## [289] "V289" "V290" "V291" "V292" "V293" "V294" "V295" "V296" "V297"
## [298] "V298" "V299" "V300" "V301" "V302" "V303" "V304" "V305" "V306"
## [307] "V307" "V308" "V309" "V310" "V311" "V312" "V313" "V314" "V315"
## [316] "V316" "V317" "V318" "V319" "V320" "V321" "V322" "V323" "V324"
## [325] "V325" "V326" "V327" "V328" "V329" "V330" "V331" "V332" "V333"
## [334] "V334" "V335" "V336" "V337" "V338" "V339" "V340" "V341" "V342"
## [343] "V343" "V344" "V345" "V346" "V347" "V348" "V349" "V350" "V351"
## [352] "V352" "V353" "V354" "V355" "V356" "V357" "V358" "V359" "V360"
## [361] "V361" "V362" "V363" "V364" "V365" "V366" "V367" "V368" "V369"
## [370] "V370" "V371" "V372" "V373" "V374" "V375" "V376" "V377" "V378"
## [379] "V379" "V380" "V381" "V382" "V383" "V384" "V385" "V386" "V387"
## [388] "V388" "V389" "V390" "V391" "V392" "V393" "V394" "V395" "V396"
## [397] "V397" "V398" "V399" "V400" "V401" "V402" "V403" "V404" "V405"
## [406] "V406" "V407" "V408" "V409" "V410" "V411" "V412" "V413" "V414"
## [415] "V415" "V416" "V417" "V418" "V419" "V420" "V421" "V422" "V423"
## [424] "V424" "V425" "V426" "V427" "V428" "V429" "V430" "V431" "V432"
## [433] "V433" "V434" "V435" "V436" "V437" "V438" "V439" "V440" "V441"
## [442] "V442" "V443" "V444" "V445" "V446" "V447" "V448" "V449" "V450"
## [451] "V451" "V452" "V453" "V454" "V455" "V456" "V457" "V458" "V459"
## [460] "V460" "V461" "V462" "V463" "V464" "V465" "V466" "V467" "V468"
## [469] "V469" "V470" "V471" "V472" "V473" "V474" "V475" "V476" "V477"
## [478] "V478" "V479" "V480" "V481" "V482" "V483" "V484" "V485" "V486"
## [487] "V487" "V488" "V489" "V490" "V491" "V492" "V493" "V494" "V495"
## [496] "V496" "V497" "V498" "V499" "V500" "V501" "V502" "V503" "V504"
## [505] "V505" "V506" "V507" "V508" "V509" "V510" "V511" "V512" "V513"
## [514] "V514" "V515" "V516" "V517" "V518" "V519" "V520" "V521" "V522"
## [523] "V523" "V524" "V525" "V526" "V527" "V528" "V529" "V530" "V531"
## [532] "V532" "V533" "V534" "V535" "V536" "V537" "V538" "V539" "V540"
## [541] "V541" "V542" "V543" "V544" "V545" "V546" "V547" "V548" "V549"
## [550] "V550" "V551" "V552" "V553" "V554" "V555" "V556" "V557" "V558"
## [559] "V559" "V560" "V561" "V562" "V563" "V564" "V565" "V566" "V567"
## [568] "V568" "V569" "V570" "V571" "V572" "V573" "V574" "V575" "V576"
## [577] "V577" "V578" "V579" "V580" "V581" "V582" "V583" "V584" "V585"
## [586] "V586" "V587" "V588" "V589" "V590" "V591" "V592" "V593" "V594"
## [595] "V595" "V596" "V597" "V598" "V599" "V600" "V601" "V602" "V603"
## [604] "V604" "V605" "V606" "V607" "V608" "V609" "V610" "V611" "V612"
## [613] "V613" "V614" "V615" "V616" "V617" "V618" "V619" "V620" "V621"
## [622] "V622" "V623" "V624" "V625" "V626" "V627" "V628" "V629" "V630"
## [631] "V631" "V632" "V633" "V634" "V635" "V636" "V637" "V638" "V639"
## [640] "V640" "V641" "V642" "V643" "V644" "V645" "V646" "V647" "V648"
## [649] "V649" "V650" "V651" "V652" "V653" "V654" "V655" "V656" "V657"
## [658] "V658" "V659" "V660" "V661" "V662" "V663" "V664" "V665" "V666"
## [667] "V667" "V668" "V669" "V670" "V671" "V672" "V673" "V674" "V675"
## [676] "V676" "V677" "V678" "V679" "V680" "V681" "V682" "V683" "V684"
## [685] "V685" "V686" "V687" "V688" "V689" "V690" "V691" "V692" "V693"
## [694] "V694" "V695" "V696" "V697" "V698" "V699" "V700" "V701" "V702"
## [703] "V703" "V704" "V705" "V706" "V707" "V708" "V709" "V710" "V711"
## [712] "V712" "V713" "V714" "V715" "V716" "V717" "V718" "V719" "V720"
## [721] "V721" "V722" "V723" "V724" "V725" "V726" "V727" "V728" "V729"
## [730] "V730" "V731" "V732" "V733" "V734" "V735" "V736" "V737" "V738"
## [739] "V739" "V740" "V741" "V742" "V743" "V744" "V745" "V746" "V747"
## [748] "V748" "V749" "V750" "V751" "V752" "V753" "V754" "V755" "V756"
## [757] "V757" "V758" "V759" "V760" "V761" "V762" "V763" "V764" "V765"
## [766] "V766" "V767" "V768" "V769" "V770" "V771" "V772" "V773" "V774"
## [775] "V775" "V776" "V777" "V778" "V779" "V780" "V781" "V782" "V783"
## [784] "V784" "V785" "V786" "V787" "V788" "V789" "V790" "V791" "V792"
## [793] "V793" "V794" "V795" "V796" "V797" "V798" "V799" "V800" "V801"
## [802] "V802" "V803" "V804" "V805" "V806" "V807" "V808" "V809" "V810"
## [811] "V811" "V812" "V813" "V814" "V815" "V816" "V817" "V818" "V819"
## [820] "V820" "V821" "V822" "V823" "V824" "V825" "V826" "V827" "V828"
## [829] "V829" "V830" "V831" "V832" "V833" "V834" "V835" "V836" "V837"
## [838] "V838" "V839" "V840" "V841" "V842" "V843" "V844" "V845" "V846"
## [847] "V847" "V848" "V849" "V850" "V851" "V852" "V853" "V854" "V855"
## [856] "V856" "V857" "V858" "V859" "V860" "V861" "V862" "V863" "V864"
## [865] "V865" "V866" "V867" "V868" "V869" "V870" "V871" "V872" "V873"
## [874] "V874" "V875" "V876" "V877" "V878" "V879" "V880" "V881" "V882"
## [883] "V883" "V884" "V885" "V886" "V887" "V888" "V889" "V890" "V891"
## [892] "V892" "V893" "V894" "V895" "V896" "V897" "V898" "V899" "V900"
## [901] "V901" "V902" "V903" "V904" "V905" "V906" "V907" "V908" "V909"
## [910] "V910" "V911" "V912" "V913" "V914" "V915" "V916" "V917" "V918"
## [919] "V919" "V920" "V921" "V922" "V923" "V924" "V925" "V926" "V927"
## [928] "V928" "V929" "V930" "V931" "V932" "V933" "V934" "V935" "V936"
## [937] "V937" "V938" "V939" "V940" "V941" "V942" "V943" "V944" "V945"
## [946] "V946" "V947" "V948" "V949" "V950" "V951" "V952" "V953" "V954"
## [955] "V955" "V956" "V957" "V958" "V959" "V960" "V961" "V962" "V963"
## [964] "V964" "V965" "V966" "V967" "V968" "V969" "V970" "V971" "V972"
## [973] "V973" "V974" "V975" "V976" "V977" "V978" "V979" "V980" "V981"
## [982] "V982" "V983" "V984" "V985" "V986" "V987" "V988" "V989" "V990"
## [991] "V991" "V992" "V993" "V994" "V995" "V996" "V997" "V998" "V999"
## [1000] "V1000" "V1001" "V1002" "V1003" "V1004" "V1005" "V1006" "V1007" "V1008"
## [1009] "V1009" "V1010" "V1011" "V1012" "V1013" "V1014" "V1015" "V1016" "V1017"
## [1018] "V1018" "V1019" "V1020" "V1021" "V1022" "V1023" "V1024" "V1025" "V1026"
## [1027] "V1027" "V1028" "V1029" "V1030" "V1031" "V1032" "V1033" "V1034" "V1035"
## [1036] "V1036" "V1037" "V1038" "V1039" "V1040" "V1041" "V1042" "V1043" "V1044"
## [1045] "V1045" "V1046" "V1047" "V1048" "V1049" "V1050" "V1051" "V1052" "V1053"
## [1054] "V1054" "V1055" "V1056" "V1057" "V1058" "V1059" "V1060" "V1061" "V1062"
## [1063] "V1063" "V1064" "V1065" "V1066" "V1067" "V1068" "V1069" "V1070" "V1071"
## [1072] "V1072" "V1073" "V1074" "V1075" "V1076" "V1077" "V1078" "V1079" "V1080"
## [1081] "V1081" "V1082" "V1083" "V1084" "V1085" "V1086" "V1087" "V1088" "V1089"
## [1090] "V1090" "V1091" "V1092" "V1093" "V1094" "V1095" "V1096" "V1097" "V1098"
## [1099] "V1099" "V1100" "V1101" "V1102" "V1103" "V1104" "V1105" "V1106" "V1107"
## [1108] "V1108" "V1109" "V1110" "V1111" "V1112" "V1113" "V1114" "V1115" "V1116"
## [1117] "V1117" "V1118" "V1119" "V1120" "V1121" "V1122" "V1123" "V1124" "V1125"
## [1126] "V1126" "V1127" "V1128" "V1129" "V1130" "V1131" "V1132" "V1133" "V1134"
## [1135] "V1135" "V1136" "V1137" "V1138" "V1139" "V1140" "V1141" "V1142" "V1143"
## [1144] "V1144" "V1145" "V1146" "V1147" "V1148" "V1149" "V1150" "V1151" "V1152"
## [1153] "V1153" "V1154" "V1155" "V1156" "V1157" "V1158" "V1159" "V1160" "V1161"
## [1162] "V1162" "V1163" "V1164" "V1165" "V1166" "V1167" "V1168" "V1169" "V1170"
## [1171] "V1171" "V1172" "V1173" "V1174" "V1175" "V1176" "V1177" "V1178" "V1179"
## [1180] "V1180" "V1181" "V1182" "V1183" "V1184" "V1185" "V1186" "V1187" "V1188"
## [1189] "V1189" "V1190" "V1191" "V1192" "V1193" "V1194" "V1195" "V1196" "V1197"
## [1198] "V1198" "V1199" "V1200" "V1201" "V1202" "V1203" "V1204" "V1205" "V1206"
## [1207] "V1207" "V1208" "V1209" "V1210" "V1211" "V1212" "V1213" "V1214" "V1215"
## [1216] "V1216" "V1217" "V1218" "V1219" "V1220" "V1221" "V1222" "V1223" "V1224"
## [1225] "V1225" "V1226" "V1227" "V1228" "V1229" "V1230" "V1231" "V1232" "V1233"
## [1234] "V1234" "V1235" "V1236" "V1237" "V1238" "V1239" "V1240" "V1241" "V1242"
## [1243] "V1243" "V1244" "V1245" "V1246" "V1247" "V1248" "V1249" "V1250" "V1251"
## [1252] "V1252" "V1253" "V1254" "V1255" "V1256" "V1257" "V1258" "V1259" "V1260"
## [1261] "V1261" "V1262" "V1263" "V1264" "V1265" "V1266" "V1267" "V1268" "V1269"
## [1270] "V1270" "V1271" "V1272" "V1273" "V1274" "V1275" "V1276" "V1277" "V1278"
## [1279] "V1279" "V1280" "V1281" "V1282" "V1283" "V1284" "V1285" "V1286" "V1287"
## [1288] "V1288" "V1289" "V1290" "V1291" "V1292" "V1293" "V1294" "V1295" "V1296"
## [1297] "V1297" "V1298" "V1299" "V1300" "V1301" "V1302" "V1303" "V1304" "V1305"
## [1306] "V1306" "V1307" "V1308" "V1309" "V1310" "V1311" "V1312" "V1313" "V1314"
## [1315] "V1315" "V1316" "V1317" "V1318" "V1319" "V1320" "V1321" "V1322" "V1323"
## [1324] "V1324" "V1325" "V1326" "V1327" "V1328" "V1329" "V1330" "V1331" "V1332"
## [1333] "V1333" "V1334" "V1335" "V1336" "V1337" "V1338" "V1339" "V1340" "V1341"
## [1342] "V1342" "V1343" "V1344" "V1345" "V1346" "V1347" "V1348" "V1349" "V1350"
## [1351] "V1351" "V1352" "V1353" "V1354" "V1355" "V1356" "V1357" "V1358" "V1359"
## [1360] "V1360" "V1361" "V1362" "V1363" "V1364" "V1365" "V1366" "V1367" "V1368"
## [1369] "V1369" "V1370" "V1371" "V1372" "V1373" "V1374" "V1375" "V1376" "V1377"
## [1378] "V1378" "V1379" "V1380" "V1381" "V1382" "V1383" "V1384" "V1385" "V1386"
## [1387] "V1387" "V1388" "V1389" "V1390" "V1391" "V1392" "V1393" "V1394" "V1395"
## [1396] "V1396" "V1397" "V1398" "V1399" "V1400" "V1401" "V1402" "V1403" "V1404"
## [1405] "V1405" "V1406" "V1407" "V1408" "V1409" "V1410" "V1411" "V1412" "V1413"
## [1414] "V1414" "V1415" "V1416" "V1417" "V1418" "V1419" "V1420" "V1421" "V1422"
## [1423] "V1423" "V1424" "V1425" "V1426" "V1427" "V1428" "V1429" "V1430" "V1431"
## [1432] "V1432" "V1433" "V1434" "V1435" "V1436" "V1437" "V1438" "V1439" "V1440"
## [1441] "V1441" "V1442" "V1443" "V1444" "V1445" "V1446" "V1447" "V1448" "V1449"
## [1450] "V1450" "V1451" "V1452" "V1453" "V1454" "V1455" "V1456" "V1457" "V1458"
## [1459] "V1459" "V1460" "V1461" "V1462" "V1463" "V1464" "V1465" "V1466" "V1467"
## [1468] "V1468" "V1469" "V1470" "V1471" "V1472" "V1473" "V1474" "V1475" "V1476"
## [1477] "V1477" "V1478" "V1479" "V1480" "V1481" "V1482" "V1483" "V1484" "V1485"
## [1486] "V1486" "V1487" "V1488" "V1489" "V1490" "V1491" "V1492" "V1493" "V1494"
## [1495] "V1495" "V1496" "V1497" "V1498" "V1499" "V1500" "V1501" "V1502" "V1503"
## [1504] "V1504" "V1505" "V1506" "V1507" "V1508" "V1509" "V1510" "V1511" "V1512"
## [1513] "V1513" "V1514" "V1515" "V1516" "V1517" "V1518" "V1519" "V1520" "V1521"
## [1522] "V1522" "V1523" "V1524" "V1525" "V1526" "V1527" "V1528" "V1529" "V1530"
## [1531] "V1531" "V1532" "V1533" "V1534" "V1535" "V1536" "V1537" "V1538" "V1539"
## [1540] "V1540" "V1541" "V1542" "V1543" "V1544" "V1545" "V1546" "V1547" "V1548"
## [1549] "V1549" "V1550" "V1551" "V1552" "V1553" "V1554" "V1555" "V1556" "V1557"
## [1558] "V1558" "V1559" "V1560" "V1561" "V1562" "V1563" "V1564" "V1565" "V1566"
## [1567] "V1567" "V1568" "V1569" "V1570" "V1571" "V1572" "V1573" "V1574" "V1575"
## [1576] "V1576" "V1577" "V1578" "V1579" "V1580" "V1581" "V1582" "V1583" "V1584"
## [1585] "V1585" "V1586" "V1587" "V1588" "V1589" "V1590" "V1591" "V1592" "V1593"
## [1594] "V1594" "V1595" "V1596" "V1597" "V1598" "V1599" "V1600" "V1601" "V1602"
## [1603] "V1603" "V1604" "V1605" "V1606" "V1607" "V1608" "V1609" "V1610" "V1611"
## [1612] "V1612" "V1613" "V1614" "V1615" "V1616" "V1617" "V1618" "V1619" "V1620"
## [1621] "V1621" "V1622" "V1623" "V1624" "V1625" "V1626" "V1627" "V1628" "V1629"
## [1630] "V1630" "V1631" "V1632" "V1633" "V1634" "V1635" "V1636" "V1637" "V1638"
## [1639] "V1639" "V1640" "V1641" "V1642" "V1643" "V1644" "V1645" "V1646" "V1647"
## [1648] "V1648" "V1649" "V1650" "V1651" "V1652" "V1653" "V1654" "V1655" "V1656"
## [1657] "V1657" "V1658" "V1659" "V1660" "V1661" "V1662" "V1663" "V1664" "V1665"
## [1666] "V1666" "V1667" "V1668" "V1669" "V1670" "V1671" "V1672" "V1673" "V1674"
## [1675] "V1675" "V1676" "V1677" "V1678" "V1679" "V1680" "V1681" "V1682" "V1683"
## [1684] "V1684" "V1685" "V1686" "V1687" "V1688" "V1689" "V1690" "V1691" "V1692"
## [1693] "V1693" "V1694" "V1695" "V1696" "V1697" "V1698" "V1699" "V1700" "V1701"
## [1702] "V1702" "V1703" "V1704" "V1705" "V1706" "V1707" "V1708" "V1709" "V1710"
## [1711] "V1711" "V1712" "V1713" "V1714" "V1715" "V1716" "V1717" "V1718" "V1719"
## [1720] "V1720" "V1721" "V1722" "V1723" "V1724" "V1725" "V1726" "V1727" "V1728"
## [1729] "V1729" "V1730" "V1731" "V1732" "V1733" "V1734" "V1735" "V1736" "V1737"
## [1738] "V1738" "V1739" "V1740" "V1741" "V1742" "V1743" "V1744" "V1745" "V1746"
## [1747] "V1747" "V1748" "V1749" "V1750" "V1751" "V1752" "V1753" "V1754" "V1755"
## [1756] "V1756" "V1757" "V1758" "V1759" "V1760" "V1761" "V1762" "V1763" "V1764"
## [1765] "V1765" "V1766" "V1767" "V1768" "V1769" "V1770" "V1771" "V1772" "V1773"
## [1774] "V1774" "V1775" "V1776" "V1777" "V1778" "V1779" "V1780" "V1781" "V1782"
## [1783] "V1783" "V1784" "V1785" "V1786" "V1787" "V1788" "V1789" "V1790" "V1791"
## [1792] "V1792" "V1793" "V1794" "V1795" "V1796" "V1797" "V1798" "V1799" "V1800"
## [1801] "V1801" "V1802" "V1803" "V1804" "V1805" "V1806" "V1807" "V1808" "V1809"
## [1810] "V1810" "V1811" "V1812" "V1813" "V1814" "V1815" "V1816" "V1817" "V1818"
## [1819] "V1819" "V1820" "V1821" "V1822" "V1823" "V1824" "V1825" "V1826" "V1827"
## [1828] "V1828" "V1829" "V1830" "V1831" "V1832" "V1833" "V1834" "V1835" "V1836"
## [1837] "V1837" "V1838" "V1839" "V1840" "V1841" "V1842" "V1843" "V1844" "V1845"
## [1846] "V1846" "V1847" "V1848" "V1849" "V1850" "V1851" "V1852" "V1853" "V1854"
## [1855] "V1855" "V1856" "V1857" "V1858" "V1859" "V1860" "V1861" "V1862" "V1863"
## [1864] "V1864" "V1865" "V1866" "V1867" "V1868" "V1869" "V1870" "V1871" "V1872"
## [1873] "V1873" "V1874" "V1875" "V1876" "V1877" "V1878" "V1879" "V1880" "V1881"
## [1882] "V1882" "V1883" "V1884" "V1885" "V1886" "V1887" "V1888" "V1889" "V1890"
## [1891] "V1891" "V1892" "V1893" "V1894" "V1895" "V1896" "V1897" "V1898" "V1899"
## [1900] "V1900" "V1901" "V1902" "V1903" "V1904" "V1905" "V1906" "V1907" "V1908"
## [1909] "V1909" "V1910" "V1911" "V1912" "V1913" "V1914" "V1915" "V1916" "V1917"
## [1918] "V1918" "V1919" "V1920" "V1921" "V1922" "V1923" "V1924" "V1925" "V1926"
## [1927] "V1927" "V1928" "V1929" "V1930" "V1931" "V1932" "V1933" "V1934" "V1935"
## [1936] "V1936" "V1937" "V1938" "V1939" "V1940" "V1941" "V1942" "V1943" "V1944"
## [1945] "V1945" "V1946" "V1947" "V1948" "V1949" "V1950" "V1951" "V1952" "V1953"
## [1954] "V1954" "V1955" "V1956" "V1957" "V1958" "V1959" "V1960" "V1961" "V1962"
## [1963] "V1963" "V1964" "V1965" "V1966" "V1967" "V1968" "V1969" "V1970" "V1971"
## [1972] "V1972" "V1973" "V1974" "V1975" "V1976" "V1977" "V1978" "V1979" "V1980"
## [1981] "V1981" "V1982" "V1983" "V1984" "V1985" "V1986" "V1987" "V1988" "V1989"
## [1990] "V1990" "V1991" "V1992" "V1993" "V1994" "V1995" "V1996" "V1997" "V1998"
## [1999] "V1999" "V2000" "V2001" "V2002" "V2003" "V2004" "V2005" "V2006" "V2007"
## [2008] "V2008" "V2009" "V2010" "V2011" "V2012" "V2013" "V2014" "V2015" "V2016"
## [2017] "V2017" "V2018" "V2019" "V2020" "V2021" "V2022" "V2023" "V2024" "V2025"
## [2026] "V2026" "V2027" "V2028" "V2029" "V2030" "V2031" "V2032" "V2033" "V2034"
## [2035] "V2035" "V2036" "V2037" "V2038" "V2039" "V2040" "V2041" "V2042" "V2043"
## [2044] "V2044" "V2045" "V2046" "V2047" "V2048" "V2049" "V2050" "V2051" "V2052"
## [2053] "V2053" "V2054" "V2055" "V2056" "V2057" "V2058" "V2059" "V2060" "V2061"
## [2062] "V2062" "V2063" "V2064" "V2065" "V2066" "V2067" "V2068" "V2069" "V2070"
## [2071] "V2071" "V2072" "V2073" "V2074" "V2075" "V2076" "V2077" "V2078" "V2079"
## [2080] "V2080" "V2081" "V2082" "V2083" "V2084" "V2085" "V2086" "V2087" "V2088"
## [2089] "V2089" "V2090" "V2091" "V2092" "V2093" "V2094" "V2095" "V2096" "V2097"
## [2098] "V2098" "V2099" "V2100" "V2101" "V2102" "V2103" "V2104" "V2105" "V2106"
## [2107] "V2107" "V2108" "V2109" "V2110" "V2111" "V2112" "V2113" "V2114" "V2115"
## [2116] "V2116" "V2117" "V2118" "V2119" "V2120" "V2121" "V2122" "V2123" "V2124"
## [2125] "V2125" "V2126" "V2127" "V2128" "V2129" "V2130" "V2131" "V2132" "V2133"
## [2134] "V2134" "V2135" "V2136" "V2137" "V2138" "V2139" "V2140" "V2141" "V2142"
## [2143] "V2143" "V2144" "V2145" "V2146" "V2147" "V2148" "V2149" "V2150" "V2151"
## [2152] "V2152" "V2153" "V2154" "V2155" "V2156" "V2157" "V2158" "V2159" "V2160"
## [2161] "V2161" "V2162" "V2163" "V2164" "V2165" "V2166" "V2167" "V2168" "V2169"
## [2170] "V2170" "V2171" "V2172" "V2173" "V2174" "V2175" "V2176" "V2177" "V2178"
## [2179] "V2179" "V2180" "V2181" "V2182" "V2183" "V2184" "V2185" "V2186" "V2187"
## [2188] "V2188" "V2189" "V2190" "V2191" "V2192" "V2193" "V2194" "V2195" "V2196"
## [2197] "V2197" "V2198" "V2199" "V2200" "V2201" "V2202" "V2203" "V2204" "V2205"
## [2206] "V2206" "V2207" "V2208" "V2209" "V2210" "V2211" "V2212" "V2213" "V2214"
## [2215] "V2215" "V2216" "V2217" "V2218" "V2219" "V2220" "V2221" "V2222" "V2223"
## [2224] "V2224" "V2225" "V2226" "V2227" "V2228" "V2229" "V2230" "V2231" "V2232"
## [2233] "V2233" "V2234" "V2235" "V2236" "V2237" "V2238" "V2239" "V2240" "V2241"
## [2242] "V2242" "V2243" "V2244" "V2245" "V2246" "V2247" "V2248" "V2249" "V2250"
## [2251] "V2251" "V2252" "V2253" "V2254" "V2255" "V2256" "V2257" "V2258" "V2259"
## [2260] "V2260" "V2261" "V2262" "V2263" "V2264" "V2265" "V2266" "V2267" "V2268"
## [2269] "V2269" "V2270" "V2271" "V2272" "V2273" "V2274" "V2275" "V2276" "V2277"
## [2278] "V2278" "V2279" "V2280" "V2281" "V2282" "V2283" "V2284" "V2285" "V2286"
## [2287] "V2287" "V2288" "V2289" "V2290" "V2291" "V2292" "V2293" "V2294" "V2295"
## [2296] "V2296" "V2297" "V2298" "V2299" "V2300" "V2301" "V2302" "V2303" "V2304"
## [2305] "V2305" "V2306" "V2307" "V2308" "V2309" "V2310" "V2311" "V2312" "V2313"
## [2314] "V2314" "V2315" "V2316" "V2317" "V2318" "V2319" "V2320" "V2321" "V2322"
## [2323] "V2323" "V2324" "V2325" "V2326" "V2327" "V2328" "V2329" "V2330" "V2331"
## [2332] "V2332" "V2333" "V2334" "V2335" "V2336" "V2337" "V2338" "V2339" "V2340"
## [2341] "V2341" "V2342" "V2343" "V2344" "V2345" "V2346" "V2347" "V2348" "V2349"
## [2350] "V2350" "V2351" "V2352" "V2353" "V2354" "V2355" "V2356" "V2357" "V2358"
## [2359] "V2359" "V2360" "V2361" "V2362" "V2363" "V2364" "V2365" "V2366" "V2367"
## [2368] "V2368" "V2369" "V2370" "V2371" "V2372" "V2373" "V2374" "V2375" "V2376"
## [2377] "V2377" "V2378" "V2379" "V2380" "V2381" "V2382" "V2383" "V2384" "V2385"
## [2386] "V2386" "V2387" "V2388" "V2389" "V2390" "V2391" "V2392" "V2393" "V2394"
## [2395] "V2395" "V2396" "V2397" "V2398" "V2399" "V2400" "V2401" "V2402" "V2403"
## [2404] "V2404" "V2405" "V2406" "V2407" "V2408" "V2409" "V2410" "V2411" "V2412"
## [2413] "V2413" "V2414" "V2415" "V2416" "V2417" "V2418" "V2419" "V2420" "V2421"
## [2422] "V2422" "V2423" "V2424" "V2425" "V2426" "V2427" "V2428" "V2429" "V2430"
## [2431] "V2431" "V2432" "V2433" "V2434" "V2435" "V2436" "V2437" "V2438" "V2439"
## [2440] "V2440" "V2441" "V2442" "V2443" "V2444" "V2445" "V2446" "V2447" "V2448"
## [2449] "V2449" "V2450" "V2451" "V2452" "V2453" "V2454" "V2455" "V2456" "V2457"
## [2458] "V2458" "V2459" "V2460" "V2461" "V2462" "V2463" "V2464" "V2465" "V2466"
## [2467] "V2467" "V2468" "V2469" "V2470" "V2471" "V2472" "V2473" "V2474" "V2475"
## [2476] "V2476" "V2477" "V2478" "V2479" "V2480" "V2481" "V2482" "V2483" "V2484"
## [2485] "V2485" "V2486" "V2487" "V2488" "V2489" "V2490" "V2491" "V2492" "V2493"
## [2494] "V2494" "V2495" "V2496" "V2497" "V2498" "V2499" "V2500" "V2501" "V2502"
## [2503] "V2503" "V2504" "V2505" "V2506" "V2507" "V2508" "V2509" "V2510" "V2511"
## [2512] "V2512" "V2513" "V2514" "V2515" "V2516" "V2517" "V2518" "V2519" "V2520"
## [2521] "V2521" "V2522" "V2523" "V2524" "V2525" "V2526" "V2527" "V2528" "V2529"
## [2530] "V2530" "V2531" "V2532" "V2533" "V2534" "V2535" "V2536" "V2537" "V2538"
## [2539] "V2539" "V2540" "V2541" "V2542" "V2543" "V2544" "V2545" "V2546" "V2547"
## [2548] "V2548" "V2549" "V2550" "V2551" "V2552" "V2553" "V2554" "V2555" "V2556"
## [2557] "V2557" "V2558" "V2559" "V2560" "V2561" "V2562" "V2563" "V2564" "V2565"
## [2566] "V2566" "V2567" "V2568" "V2569" "V2570" "V2571" "V2572" "V2573" "V2574"
## [2575] "V2575" "V2576" "V2577" "V2578" "V2579" "V2580" "V2581" "V2582" "V2583"
## [2584] "V2584" "V2585" "V2586" "V2587" "V2588" "V2589" "V2590" "V2591" "V2592"
## [2593] "V2593" "V2594" "V2595" "V2596" "V2597" "V2598" "V2599" "V2600" "V2601"
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##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200# las filas son el número de puntos de evaluación de la función y las columnas el número de curvas
# Suavizamiento spline
# Creación de la base de funciones
npuntos <- nrow(imagenesEnsxeydf)
base_bspline <- create.bspline.basis(c(1, npuntos), 150)
plot(base_bspline)
summary(base_bspline)##
## Basis object:
##
## Type: bspline
##
## Range: 1 to 200
##
## Number of basis functions: 150# Selección de lambda
gcv <- NULL
lambda_list <- seq(-2, 4, length.out = 30)
# estos son las potencias de 10 de variación del lambda
# inicia con valores muy cercanos a cero 10^-2 hasta 10^4
matrix_data <- as.matrix(imagenesEnsxeydf)
for (lambda in lambda_list) {
lambda <- 10**lambda
param_lambda <- fdPar(base_bspline,
2,
lambda)
fd_smoothEns <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
gcv <- c(gcv, sum(fd_smoothEns$gcv))
}
# Se esta tomando como criterio total la suma de las VC de todas las curvas
plotgcv <-
data.frame(log_lambda = lambda_list,
gcv = gcv) %>% ggplot(aes(x = log_lambda, y = gcv)) +
geom_line(color = "purple", linetype = "dashed") +
theme_light() +
scale_x_continuous(limits = c(0, 4))
plotly::ggplotly(plotgcv)selected_lamdba <- 10**lambda_list[which.min(gcv)]
print(selected_lamdba)## [1] 0.02592944print(log(selected_lamdba, 10))## [1] -1.586207# entrega el valor de lambda donde se minimiza la suma de VC y log de lambda
# Suavizamiento con el lambda elegido
param_lambda <- fdPar(base_bspline,
2,
lambda = selected_lamdba)
fd_smoothEns <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
plot(fd_smoothEns, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(fd_smoothEns$fd[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
fd_fittedEns <- fitted(fd_smoothEns)
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedEns[,1],col="2")
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))4. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)
Corre en 2 minutos.
Por Curvas: La fracción de acierto global es de 0.5847222, para imágenes dúctiles es de 0.8183333, para imágenes frágiles de 0.6975 y para imágenes de fatiga 0.2383333.
Por fotos: La fracción de acierto global es de 0.7777,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Las tres frágiles se adjudican correctamente. Acierto 100%. Una de fatiga se adjudicó correcta. Acierto 33.3%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(fd_smoothEnt$fd,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsfdata <- fdata(fd_smoothEns$fd,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 982 44 174
## 2 132 836 232
## 3 183 730 287t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5847222t[1,1]/1200## [1] 0.8183333t[2,2]/1200## [1] 0.6966667t[3,3]/1200## [1] 0.2391667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 299 17 84
## [3,] 289 27 84# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 236 104
## [2,] 2 395 3
## [3,] 70 205 125# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 47 249 104
## [2,] 129 134 137
## [3,] 7 347 46# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
5. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (Usando datos sin suavizamiento) con familia binomial
Corre en 2 minutos.
La fracción de acierto global es de 0.5852778, para imágenes dúctiles es de 0.8183333, para imágenes frágiles de 0.6975y para imágenes de fatiga 0.24.
Por fotos: La fracción de acierto global es de 0.7777,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Las tres frágiles se adjudican correctamente. Acierto 100%. Una de fatiga se adjudicó correcta. Acierto 33.3%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
matrix_data <- as.matrix(imagenesEntxeydf)
fd_smoothEntfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
matrix_data <- as.matrix(imagenesEnsxeydf)
fd_smoothEnsfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 982 44 174
## 2 132 836 232
## 3 183 729 288t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.585t[1,1]/1200## [1] 0.8183333t[2,2]/1200## [1] 0.6966667t[3,3]/1200## [1] 0.24Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 299 17 84
## [3,] 289 27 84# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 236 104
## [2,] 2 395 3
## [3,] 70 205 125# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 47 249 104
## [2,] 129 133 138
## [3,] 7 347 46# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
6. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (Usando datos sin suavizamiento) con familia gaussian
Corre en 2 minutos.
La fracción de acierto global es de 0.5880556, para imágenes dúctiles es de 0.8308333, para imágenes frágiles de 0.6891667 y para imágenes de fatiga 0.2441667.
Por fotos: La fracción de acierto global es de 0.666,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Las tres frágiles se adjudican correctamente. Acierto 100%. Cero de fatiga se adjudicó correcta. Acierto 0%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
matrix_data <- as.matrix(imagenesEntxeydf)
fd_smoothEntfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = gaussian()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades de clasificacion
matrix_data <- as.matrix(imagenesEnsxeydf)
fd_smoothEnsfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 997 45 158
## 2 140 826 234
## 3 198 709 293t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5877778t[1,1]/1200## [1] 0.8308333t[2,2]/1200## [1] 0.6883333t[3,3]/1200## [1] 0.2441667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 303 19 78
## [3,] 300 26 74# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 64 233 103
## [2,] 3 389 8
## [3,] 73 204 123# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 46 227 127
## [2,] 142 130 128
## [3,] 10 352 38# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
7. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (Usando datos sin suavizamiento) con familia poisson
Corre en 2 minutos.
La fracción de acierto global es de 0.5861111 para imágenes dúctiles es de 0.71, para imágenes frágiles de 0.5516667 y para imágenes de fatiga 0.4966667.
Por fotos: La fracción de acierto global es de 0.666,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Uno de tres frágiles se adjudican correctamente. Acierto 33%. Dos de tres de fatiga se adjudicó correcta. Acierto 66%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
matrix_data <- as.matrix(imagenesEntxeydf)
fd_smoothEntfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = poisson()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
matrix_data <- as.matrix(imagenesEnsxeydf)
fd_smoothEnsfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 852 6 342
## 2 76 661 463
## 3 88 516 596t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5858333t[1,1]/1200## [1] 0.71t[2,2]/1200## [1] 0.5508333t[3,3]/1200## [1] 0.4966667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 388 0 12
## [2,] 217 1 182
## [3,] 247 5 148# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 38 173 189
## [2,] 1 362 37
## [3,] 37 126 237# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 22 154 224
## [2,] 66 56 278
## [3,] 0 306 94# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
8. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (Usando datos sin suavizamiento) con familia quasi
Corre en 2 minutos.
La fracción de acierto global es de 0.5908333, para imágenes dúctiles es de 0.8316667, para imágenes frágiles de 0.6925 y para imágenes de fatiga 0.2483333.
Por fotos: La fracción de acierto global es de 0.666,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Las tres frágiles se adjudican correctamente. Acierto 100%. Ceri de tres de fatiga se adjudicó correcta. Acierto 0%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
matrix_data <- as.matrix(imagenesEntxeydf)
fd_smoothEntfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = quasi()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
matrix_data <- as.matrix(imagenesEnsxeydf)
fd_smoothEnsfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 997 45 158
## 2 140 826 234
## 3 198 709 293t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5877778t[1,1]/1200## [1] 0.8308333t[2,2]/1200## [1] 0.6883333t[3,3]/1200## [1] 0.2441667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 303 19 78
## [3,] 300 26 74# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 64 233 103
## [2,] 3 389 8
## [3,] 73 204 123# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 46 227 127
## [2,] 142 130 128
## [3,] 10 352 38# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
9. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (Usando datos sin suavizamiento) con familia quasibinomial
Corre en 2 minutos.
La fracción de acierto global es de 0.5880556, para imágenes dúctiles es de 0.82, para imágenes frágiles de 0.7 y para imágenes de fatiga 0.2441667.
Por fotos: La fracción de acierto global es de 0.7777,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Las tres frágiles se adjudican correctamente. Acierto 100%. Una de tres de fatiga se adjudicó correcta. Acierto 33%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
matrix_data <- as.matrix(imagenesEntxeydf)
fd_smoothEntfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = quasibinomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
matrix_data <- as.matrix(imagenesEnsxeydf)
fd_smoothEnsfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 982 44 174
## 2 132 836 232
## 3 183 729 288t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.585t[1,1]/1200## [1] 0.8183333t[2,2]/1200## [1] 0.6966667t[3,3]/1200## [1] 0.24Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 299 17 84
## [3,] 289 27 84# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 236 104
## [2,] 2 395 3
## [3,] 70 205 125# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 47 249 104
## [2,] 129 133 138
## [3,] 7 347 46# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
10. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (Usando datos sin suavizamiento) con familia quasipoisson
Corre en 2 minutos.
La fracción de acierto global es de 0.5838889, para imágenes dúctiles es de 0.7108333, para imágenes frágiles de 0.5466667 y para imágenes de fatiga 0.4941667.
Por fotos: La fracción de acierto global es de 0.666,
Las tres dúctiles se adjudican a dúctil. Acierto 100%. Una de tres frágiles se adjudican correctamente. Acierto 33%. Dos de tres de fatiga se adjudicó correcta. Acierto 66%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
matrix_data <- as.matrix(imagenesEntxeydf)
fd_smoothEntfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = quasipoisson()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
matrix_data <- as.matrix(imagenesEnsxeydf)
fd_smoothEnsfdata <- fdata(t(matrix_data)) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 852 6 342
## 2 76 661 463
## 3 88 516 596t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5858333t[1,1]/1200## [1] 0.71t[2,2]/1200## [1] 0.5508333t[3,3]/1200## [1] 0.4966667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 388 0 12
## [2,] 217 1 182
## [3,] 247 5 148# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 38 173 189
## [2,] 1 362 37
## [3,] 37 126 237# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 22 154 224
## [2,] 66 56 278
## [3,] 0 306 94# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
11. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (usando curvas derivadas) familia binomial (suavizado afinado)
Corre en 2 minutos.
La fracción de acierto global es de 0.362, para imágenes dúctiles es de 0.363, para imágenes frágiles de 0.297 y para imágenes de fatiga 0.426.
Por fotos: La fracción de acierto global es de 0.333,
Una de tres dúctiles se adjudican a dúctil. Acierto 33%. Ninguna de tres frágiles se adjudican correctamente. Acierto 0%. Dos de tres de fatiga se adjudicó correcta. Acierto 66%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEnt1Der <- deriv.fd(fd_smoothEnt$fd,1)
fd_smoothEntfdata <- fdata(fd_smoothEnt1Der,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEns1Der <- deriv.fd(fd_smoothEns$fd,1)
fd_smoothEnsfdata <- fdata(fd_smoothEns1Der,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 436 316 448
## 2 486 357 357
## 3 418 270 512t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.3625t[1,1]/1200## [1] 0.3633333t[2,2]/1200## [1] 0.2975t[3,3]/1200## [1] 0.4266667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 204 90 106
## [2,] 88 135 177
## [3,] 144 91 165# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 166 112 122
## [2,] 172 114 114
## [3,] 148 131 121# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 88 94 218
## [2,] 200 89 111
## [3,] 130 87 183# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad/deriv", ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad/deriv", ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
12. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (usando curvas derivadas segundas) familia binomial (suavizado afinado)
Corre en 2 minutos.
La fracción de acierto global es de 0.359, para imágenes dúctiles es de 0.345, para imágenes frágiles de 0.382 y para imágenes de fatiga 0.352.
Por fotos: La fracción de acierto global es de 0.444,
Una de tres dúctiles se adjudican a dúctil. Acierto 33%. Dos de tres frágiles se adjudican correctamente. Acierto 66%. Una de tres de fatiga se adjudicó correcta. Acierto 33%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEnt1Der <- deriv.fd(fd_smoothEnt$fd,2)
fd_smoothEntfdata <- fdata(fd_smoothEnt1Der,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEns1Der <- deriv.fd(fd_smoothEns$fd,2)
fd_smoothEnsfdata <- fdata(fd_smoothEns1Der,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 413 468 319
## 2 380 461 359
## 3 396 379 425t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.3608333t[1,1]/1200## [1] 0.3441667t[2,2]/1200## [1] 0.3841667t[3,3]/1200## [1] 0.3541667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 139 155 106
## [2,] 126 183 91
## [3,] 148 130 122# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 125 147 128
## [2,] 106 180 114
## [3,] 149 134 117# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 128 146 126
## [2,] 108 135 157
## [3,] 160 98 142# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad/deriv2", ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad/deriv", ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
B. Con centrado de los datos
Se centran las curvas respecto a la media global funcional de cada grupo (entrenamiento y ensayo)
1. Suavizamiento Spline (afinado) y Restando media con familia binomial
Corre en 2 minutos.
La fracción de acierto global es de 0.585, para imágenes dúctiles es de 0.818 para imágenes frágiles de 0.697 y para imágenes de fatiga 0.239.
Por fotos: La fracción de acierto global es de 0.777,
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Una de tres de fatiga se adjudicó correcta. Acierto 33%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntCenter <- center.fd(fd_smoothEnt$fd)
fd_smoothEntfdata <- fdata(fd_smoothEntCenter,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsCenter <- center.fd(fd_smoothEns$fd)
fd_smoothEnsfdata <- fdata(fd_smoothEnsCenter,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 982 44 174
## 2 132 836 232
## 3 183 730 287t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5847222t[1,1]/1200## [1] 0.8183333t[2,2]/1200## [1] 0.6966667t[3,3]/1200## [1] 0.2391667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 299 17 84
## [3,] 289 27 84# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 236 104
## [2,] 2 395 3
## [3,] 70 205 125# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 47 249 104
## [2,] 129 134 137
## [3,] 7 347 46# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
2. Suavizamiento Spline (afinado) y Restando media con familia gaussian
Corre en 2 minutos.
La fracción de acierto global es de 0.588, para imágenes dúctiles es de 0.831, para imágenes frágiles de 0.689 y para imágenes de fatiga 0.244.
Por fotos: La fracción de acierto global es de 0.666,
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Ninguna de tres de fatiga se adjudicó correcta. Acierto 0%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntCenter <- center.fd(fd_smoothEnt$fd)
fd_smoothEntfdata <- fdata(fd_smoothEntCenter,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = gaussian()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsCenter <- center.fd(fd_smoothEns$fd)
fd_smoothEnsfdata <- fdata(fd_smoothEnsCenter,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 997 45 158
## 2 140 826 234
## 3 198 708 294t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5880556t[1,1]/1200## [1] 0.8308333t[2,2]/1200## [1] 0.6883333t[3,3]/1200## [1] 0.245Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 303 19 78
## [3,] 300 26 74# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 64 233 103
## [2,] 3 389 8
## [3,] 73 204 123# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 46 227 127
## [2,] 142 130 128
## [3,] 10 351 39# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
3. Suavizamiento Spline (afinado) y Restando media con familia poisson
Corre en 2 minutos.
La fracción de acierto global es de 0.586, para imágenes dúctiles es de 0.710, para imágenes frágiles de 0.552 y para imágenes de fatiga 0.497.
Por fotos: La fracción de acierto global es de 0.666,
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Uno de tres frágiles se adjudican correctamente. Acierto 33%. Dos de tres de fatiga se adjudicó correcta. Acierto 66%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntCenter <- center.fd(fd_smoothEnt$fd)
fd_smoothEntfdata <- fdata(fd_smoothEntCenter,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = poisson()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsCenter <- center.fd(fd_smoothEns$fd)
fd_smoothEnsfdata <- fdata(fd_smoothEnsCenter,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 852 6 342
## 2 76 661 463
## 3 88 516 596t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5858333t[1,1]/1200## [1] 0.71t[2,2]/1200## [1] 0.5508333t[3,3]/1200## [1] 0.4966667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 388 0 12
## [2,] 217 1 182
## [3,] 247 5 148# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 38 174 188
## [2,] 1 362 37
## [3,] 37 125 238# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 22 155 223
## [2,] 66 56 278
## [3,] 0 305 95# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
4. Suavizamiento Spline (afinado) y Restando media con familia quasi
Corre en 2 minutos.
La fracción de acierto global es de 0.588, para imágenes dúctiles es de 0.831, para imágenes frágiles de 0.689 y para imágenes de fatiga 0.244.
Por fotos: La fracción de acierto global es de 0.666,
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Cero de tres de fatiga se adjudicó correcta. Acierto 0%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntCenter <- center.fd(fd_smoothEnt$fd)
fd_smoothEntfdata <- fdata(fd_smoothEntCenter,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = quasi()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsCenter <- center.fd(fd_smoothEns$fd)
fd_smoothEnsfdata <- fdata(fd_smoothEnsCenter,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 997 45 158
## 2 140 826 234
## 3 198 708 294t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5880556t[1,1]/1200## [1] 0.8308333t[2,2]/1200## [1] 0.6883333t[3,3]/1200## [1] 0.245Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 303 19 78
## [3,] 300 26 74# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 64 233 103
## [2,] 3 389 8
## [3,] 73 204 123# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 46 227 127
## [2,] 142 130 128
## [3,] 10 351 39# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
5. Suavizamiento Spline (afinado) y Restando media con familia quasibinomial
Corre en 2 minutos.
La fracción de acierto global es de 0.585, para imágenes dúctiles es de 0.818, para imágenes frágiles de 0.697 y para imágenes de fatiga 0.239.
Por fotos: La fracción de acierto global es de 0.777.
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frágiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntCenter <- center.fd(fd_smoothEnt$fd)
fd_smoothEntfdata <- fdata(fd_smoothEntCenter,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = quasibinomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsCenter <- center.fd(fd_smoothEns$fd)
fd_smoothEnsfdata <- fdata(fd_smoothEnsCenter,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 982 44 174
## 2 132 836 232
## 3 183 730 287t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5847222t[1,1]/1200## [1] 0.8183333t[2,2]/1200## [1] 0.6966667t[3,3]/1200## [1] 0.2391667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 394 0 6
## [2,] 299 17 84
## [3,] 289 27 84# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 60 236 104
## [2,] 2 395 3
## [3,] 70 205 125# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 47 249 104
## [2,] 129 134 137
## [3,] 7 347 46# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
6. Suavizamiento Spline (afinado) y Restando media con familia quasipoisson
Corre en 2 minutos.
La fracción de acierto global es de 0.586, para imágenes dúctiles es de 0.710, para imágenes frágiles de 0.552 y para imágenes de fatiga 0.497.
Por fotos: La fracción de acierto global es de 0.666.
Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Una de tres frágiles se adjudican correctamente. Acierto 33%. Dos de tres de fatiga se adjudicó correcta. Acierto 66%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntCenter <- center.fd(fd_smoothEnt$fd)
fd_smoothEntfdata <- fdata(fd_smoothEntCenter,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = quasipoisson()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsCenter <- center.fd(fd_smoothEns$fd)
fd_smoothEnsfdata <- fdata(fd_smoothEnsCenter,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 852 6 342
## 2 76 661 463
## 3 88 516 596t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.5858333t[1,1]/1200## [1] 0.71t[2,2]/1200## [1] 0.5508333t[3,3]/1200## [1] 0.4966667Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 388 0 12
## [2,] 217 1 182
## [3,] 247 5 148# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 38 174 188
## [2,] 1 362 37
## [3,] 37 125 238# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 22 155 223
## [2,] 66 56 278
## [3,] 0 305 95# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxey[,1],type = "o",xlab="Pixel",ylab="Intensidad",ylim = c(-1,1))
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")
C. Con centrado de curvas a partir de la media de cada foto
Se centran las curvas respecto a la media de cada foto (tratando de quitar el efecto de intensidad de luz)
imagenesEntxeyMeanFoto <- imagenesEntxey
imagenesEnsxeyMeanFoto <- imagenesEnsxey
meanimagenductilent1xey <- mean(imagenesEntxey[,1:400])
meanimagenductilent2xey <- mean(imagenesEntxey[,401:800])
meanimagenductilent3xey <- mean(imagenesEntxey[,801:1200])
meanimagenductilent4xey <- mean(imagenesEntxey[,1201:1600])
meanimagenductilent5xey <- mean(imagenesEntxey[,1601:2000])
meanimagenductilent6xey <- mean(imagenesEntxey[,2001:2400])
meanimagenductilent7xey <- mean(imagenesEntxey[,2401:2800])
meanimagenductilens1xey <- mean(imagenesEnsxey[,1:400])
meanimagenductilens2xey <- mean(imagenesEnsxey[,401:800])
meanimagenductilens3xey <- mean(imagenesEnsxey[,801:1200])
imagenesEntxeyMeanFoto[,1:400] <- imagenesEntxeyMeanFoto[,1:400]-meanimagenductilent1xey
imagenesEntxeyMeanFoto[,401:800] <- imagenesEntxeyMeanFoto[,401:800]-meanimagenductilent2xey
imagenesEntxeyMeanFoto[,801:1200] <- imagenesEntxeyMeanFoto[,801:1200]-meanimagenductilent3xey
imagenesEntxeyMeanFoto[,1201:1600] <- imagenesEntxeyMeanFoto[,1201:1600]-meanimagenductilent4xey
imagenesEntxeyMeanFoto[,1601:2000] <- imagenesEntxeyMeanFoto[,1601:2000]-meanimagenductilent5xey
imagenesEntxeyMeanFoto[,2001:2400] <- imagenesEntxeyMeanFoto[,2001:2400]-meanimagenductilent6xey
imagenesEntxeyMeanFoto[,2401:2800] <- imagenesEntxeyMeanFoto[,2401:2800]-meanimagenductilent7xey
imagenesEnsxeyMeanFoto[,1:400] <- imagenesEnsxeyMeanFoto[,1:400]-meanimagenductilens1xey
imagenesEnsxeyMeanFoto[,401:800] <- imagenesEnsxeyMeanFoto[,401:800]-meanimagenductilens2xey
imagenesEnsxeyMeanFoto[,801:1200] <- imagenesEnsxeyMeanFoto[,801:1200]-meanimagenductilens3xey
meanimagenfragilent1xey <- mean(imagenesEntxey[,2801:3200])
meanimagenfragilent2xey <- mean(imagenesEntxey[,3201:3600])
meanimagenfragilent3xey <- mean(imagenesEntxey[,3601:4000])
meanimagenfragilent4xey <- mean(imagenesEntxey[,4001:4400])
meanimagenfragilent5xey <- mean(imagenesEntxey[,4401:4800])
meanimagenfragilent6xey <- mean(imagenesEntxey[,4801:5200])
meanimagenfragilent7xey <- mean(imagenesEntxey[,5201:5600])
meanimagenfragilens1xey <- mean(imagenesEnsxey[,1201:1600])
meanimagenfragilens2xey <- mean(imagenesEnsxey[,1601:2000])
meanimagenfragilens3xey <- mean(imagenesEnsxey[,2001:2400])
imagenesEntxeyMeanFoto[,2801:3200] <- imagenesEntxeyMeanFoto[,2801:3200]-meanimagenfragilent1xey
imagenesEntxeyMeanFoto[,3201:3600] <- imagenesEntxeyMeanFoto[,3201:3600]-meanimagenfragilent2xey
imagenesEntxeyMeanFoto[,3601:4000] <- imagenesEntxeyMeanFoto[,3601:4000]-meanimagenfragilent3xey
imagenesEntxeyMeanFoto[,4001:4400] <- imagenesEntxeyMeanFoto[,4001:4400]-meanimagenfragilent4xey
imagenesEntxeyMeanFoto[,4401:4800] <- imagenesEntxeyMeanFoto[,4401:4800]-meanimagenfragilent5xey
imagenesEntxeyMeanFoto[,4801:5200] <- imagenesEntxeyMeanFoto[,4801:5200]-meanimagenfragilent6xey
imagenesEntxeyMeanFoto[,5201:5600] <- imagenesEntxeyMeanFoto[,5201:5600]-meanimagenfragilent7xey
imagenesEnsxeyMeanFoto[,1201:1600] <- imagenesEnsxeyMeanFoto[,1201:1600]-meanimagenfragilens1xey
imagenesEnsxeyMeanFoto[,1601:2000] <- imagenesEnsxeyMeanFoto[,1601:2000]-meanimagenfragilens2xey
imagenesEnsxeyMeanFoto[,2001:2400] <- imagenesEnsxeyMeanFoto[,2001:2400]-meanimagenfragilens3xey
meanimagenfatigaent1xey <- mean(imagenesEntxey[,5601:6000])
meanimagenfatigaent2xey <- mean(imagenesEntxey[,6001:6400])
meanimagenfatigaent3xey <- mean(imagenesEntxey[,6401:6800])
meanimagenfatigaent4xey <- mean(imagenesEntxey[,6801:7200])
meanimagenfatigaent5xey <- mean(imagenesEntxey[,7201:7600])
meanimagenfatigaent6xey <- mean(imagenesEntxey[,7601:8000])
meanimagenfatigaent7xey <- mean(imagenesEntxey[,8001:8400])
meanimagenfatigaens1xey <- mean(imagenesEnsxey[,2401:2800])
meanimagenfatigaens2xey <- mean(imagenesEnsxey[,2801:3200])
meanimagenfatigaens3xey <- mean(imagenesEnsxey[,3201:3600])
imagenesEntxeyMeanFoto[,5601:6000] <- imagenesEntxeyMeanFoto[,5601:6000]-meanimagenfatigaent1xey
imagenesEntxeyMeanFoto[,6001:6400] <- imagenesEntxeyMeanFoto[,6001:6400]-meanimagenfatigaent2xey
imagenesEntxeyMeanFoto[,6401:6800] <- imagenesEntxeyMeanFoto[,6401:6800]-meanimagenfatigaent3xey
imagenesEntxeyMeanFoto[,6801:7200] <- imagenesEntxeyMeanFoto[,6801:7200]-meanimagenfatigaent4xey
imagenesEntxeyMeanFoto[,7201:7600] <- imagenesEntxeyMeanFoto[,7201:7600]-meanimagenfatigaent5xey
imagenesEntxeyMeanFoto[,7601:8000] <- imagenesEntxeyMeanFoto[,7601:8000]-meanimagenfatigaent6xey
imagenesEntxeyMeanFoto[,8001:8400] <- imagenesEntxeyMeanFoto[,8001:8400]-meanimagenfatigaent7xey
imagenesEnsxeyMeanFoto[,2401:2800] <- imagenesEnsxeyMeanFoto[,2401:2800]-meanimagenfatigaens1xey
imagenesEnsxeyMeanFoto[,2801:3200] <- imagenesEnsxeyMeanFoto[,2801:3200]-meanimagenfatigaens2xey
imagenesEnsxeyMeanFoto[,3201:3600] <- imagenesEnsxeyMeanFoto[,3201:3600]-meanimagenfatigaens3xey
attributes(imagenesEntxeyMeanFoto)## $dim
## [1] 200 8400# Convertir en Data Frame Entrenamiento
imagenesEntxeyMeanFotodf <- as.data.frame (imagenesEntxeyMeanFoto)
attributes(imagenesEntxeyMeanFotodf)## $names
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9"
## [10] "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18"
## [19] "V19" "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27"
## [28] "V28" "V29" "V30" "V31" "V32" "V33" "V34" "V35" "V36"
## [37] "V37" "V38" "V39" "V40" "V41" "V42" "V43" "V44" "V45"
## [46] "V46" "V47" "V48" "V49" "V50" "V51" "V52" "V53" "V54"
## [55] "V55" "V56" "V57" "V58" "V59" "V60" "V61" "V62" "V63"
## [64] "V64" "V65" "V66" "V67" "V68" "V69" "V70" "V71" "V72"
## [73] "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80" "V81"
## [82] "V82" "V83" "V84" "V85" "V86" "V87" "V88" "V89" "V90"
## [91] "V91" "V92" "V93" "V94" "V95" "V96" "V97" "V98" "V99"
## [100] "V100" "V101" "V102" "V103" "V104" "V105" "V106" "V107" "V108"
## [109] "V109" "V110" "V111" "V112" "V113" "V114" "V115" "V116" "V117"
## [118] "V118" "V119" "V120" "V121" "V122" "V123" "V124" "V125" "V126"
## [127] "V127" "V128" "V129" "V130" "V131" "V132" "V133" "V134" "V135"
## [136] "V136" "V137" "V138" "V139" "V140" "V141" "V142" "V143" "V144"
## [145] "V145" "V146" "V147" "V148" "V149" "V150" "V151" "V152" "V153"
## [154] "V154" "V155" "V156" "V157" "V158" "V159" "V160" "V161" "V162"
## [163] "V163" "V164" "V165" "V166" "V167" "V168" "V169" "V170" "V171"
## [172] "V172" "V173" "V174" "V175" "V176" "V177" "V178" "V179" "V180"
## [181] "V181" "V182" "V183" "V184" "V185" "V186" "V187" "V188" "V189"
## [190] "V190" "V191" "V192" "V193" "V194" "V195" "V196" "V197" "V198"
## [199] "V199" "V200" "V201" "V202" "V203" "V204" "V205" "V206" "V207"
## [208] "V208" "V209" "V210" "V211" "V212" "V213" "V214" "V215" "V216"
## [217] "V217" "V218" "V219" "V220" "V221" "V222" "V223" "V224" "V225"
## [226] "V226" "V227" "V228" "V229" "V230" "V231" "V232" "V233" "V234"
## [235] "V235" "V236" "V237" "V238" "V239" "V240" "V241" "V242" "V243"
## [244] "V244" "V245" "V246" "V247" "V248" "V249" "V250" "V251" "V252"
## [253] "V253" "V254" "V255" "V256" "V257" "V258" "V259" "V260" "V261"
## [262] "V262" "V263" "V264" "V265" "V266" "V267" "V268" "V269" "V270"
## [271] "V271" "V272" "V273" "V274" "V275" "V276" "V277" "V278" "V279"
## [280] "V280" "V281" "V282" "V283" "V284" "V285" "V286" "V287" "V288"
## [289] "V289" "V290" "V291" "V292" "V293" "V294" "V295" "V296" "V297"
## [298] "V298" "V299" "V300" "V301" "V302" "V303" "V304" "V305" "V306"
## [307] "V307" "V308" "V309" "V310" "V311" "V312" "V313" "V314" "V315"
## [316] "V316" "V317" "V318" "V319" "V320" "V321" "V322" "V323" "V324"
## [325] "V325" "V326" "V327" "V328" "V329" "V330" "V331" "V332" "V333"
## [334] "V334" "V335" "V336" "V337" "V338" "V339" "V340" "V341" "V342"
## [343] "V343" "V344" "V345" "V346" "V347" "V348" "V349" "V350" "V351"
## [352] "V352" "V353" "V354" "V355" "V356" "V357" "V358" "V359" "V360"
## [361] "V361" "V362" "V363" "V364" "V365" "V366" "V367" "V368" "V369"
## [370] "V370" "V371" "V372" "V373" "V374" "V375" "V376" "V377" "V378"
## [379] "V379" "V380" "V381" "V382" "V383" "V384" "V385" "V386" "V387"
## [388] "V388" "V389" "V390" "V391" "V392" "V393" "V394" "V395" "V396"
## [397] "V397" "V398" "V399" "V400" "V401" "V402" "V403" "V404" "V405"
## [406] "V406" "V407" "V408" "V409" "V410" "V411" "V412" "V413" "V414"
## [415] "V415" "V416" "V417" "V418" "V419" "V420" "V421" "V422" "V423"
## [424] "V424" "V425" "V426" "V427" "V428" "V429" "V430" "V431" "V432"
## [433] "V433" "V434" "V435" "V436" "V437" "V438" "V439" "V440" "V441"
## [442] "V442" "V443" "V444" "V445" "V446" "V447" "V448" "V449" "V450"
## [451] "V451" "V452" "V453" "V454" "V455" "V456" "V457" "V458" "V459"
## [460] "V460" "V461" "V462" "V463" "V464" "V465" "V466" "V467" "V468"
## [469] "V469" "V470" "V471" "V472" "V473" "V474" "V475" "V476" "V477"
## [478] "V478" "V479" "V480" "V481" "V482" "V483" "V484" "V485" "V486"
## [487] "V487" "V488" "V489" "V490" "V491" "V492" "V493" "V494" "V495"
## [496] "V496" "V497" "V498" "V499" "V500" "V501" "V502" "V503" "V504"
## [505] "V505" "V506" "V507" "V508" "V509" "V510" "V511" "V512" "V513"
## [514] "V514" "V515" "V516" "V517" "V518" "V519" "V520" "V521" "V522"
## [523] "V523" "V524" "V525" "V526" "V527" "V528" "V529" "V530" "V531"
## [532] "V532" "V533" "V534" "V535" "V536" "V537" "V538" "V539" "V540"
## [541] "V541" "V542" "V543" "V544" "V545" "V546" "V547" "V548" "V549"
## [550] "V550" "V551" "V552" "V553" "V554" "V555" "V556" "V557" "V558"
## [559] "V559" "V560" "V561" "V562" "V563" "V564" "V565" "V566" "V567"
## [568] "V568" "V569" "V570" "V571" "V572" "V573" "V574" "V575" "V576"
## [577] "V577" "V578" "V579" "V580" "V581" "V582" "V583" "V584" "V585"
## [586] "V586" "V587" "V588" "V589" "V590" "V591" "V592" "V593" "V594"
## [595] "V595" "V596" "V597" "V598" "V599" "V600" "V601" "V602" "V603"
## [604] "V604" "V605" "V606" "V607" "V608" "V609" "V610" "V611" "V612"
## [613] "V613" "V614" "V615" "V616" "V617" "V618" "V619" "V620" "V621"
## [622] "V622" "V623" "V624" "V625" "V626" "V627" "V628" "V629" "V630"
## [631] "V631" "V632" "V633" "V634" "V635" "V636" "V637" "V638" "V639"
## [640] "V640" "V641" "V642" "V643" "V644" "V645" "V646" "V647" "V648"
## [649] "V649" "V650" "V651" "V652" "V653" "V654" "V655" "V656" "V657"
## [658] "V658" "V659" "V660" "V661" "V662" "V663" "V664" "V665" "V666"
## [667] "V667" "V668" "V669" "V670" "V671" "V672" "V673" "V674" "V675"
## [676] "V676" "V677" "V678" "V679" "V680" "V681" "V682" "V683" "V684"
## [685] "V685" "V686" "V687" "V688" "V689" "V690" "V691" "V692" "V693"
## [694] "V694" "V695" "V696" "V697" "V698" "V699" "V700" "V701" "V702"
## [703] "V703" "V704" "V705" "V706" "V707" "V708" "V709" "V710" "V711"
## [712] "V712" "V713" "V714" "V715" "V716" "V717" "V718" "V719" "V720"
## [721] "V721" "V722" "V723" "V724" "V725" "V726" "V727" "V728" "V729"
## [730] "V730" "V731" "V732" "V733" "V734" "V735" "V736" "V737" "V738"
## [739] "V739" "V740" "V741" "V742" "V743" "V744" "V745" "V746" "V747"
## [748] "V748" "V749" "V750" "V751" "V752" "V753" "V754" "V755" "V756"
## [757] "V757" "V758" "V759" "V760" "V761" "V762" "V763" "V764" "V765"
## [766] "V766" "V767" "V768" "V769" "V770" "V771" "V772" "V773" "V774"
## [775] "V775" "V776" "V777" "V778" "V779" "V780" "V781" "V782" "V783"
## [784] "V784" "V785" "V786" "V787" "V788" "V789" "V790" "V791" "V792"
## [793] "V793" "V794" "V795" "V796" "V797" "V798" "V799" "V800" "V801"
## [802] "V802" "V803" "V804" "V805" "V806" "V807" "V808" "V809" "V810"
## [811] "V811" "V812" "V813" "V814" "V815" "V816" "V817" "V818" "V819"
## [820] "V820" "V821" "V822" "V823" "V824" "V825" "V826" "V827" "V828"
## [829] "V829" "V830" "V831" "V832" "V833" "V834" "V835" "V836" "V837"
## [838] "V838" "V839" "V840" "V841" "V842" "V843" "V844" "V845" "V846"
## [847] "V847" "V848" "V849" "V850" "V851" "V852" "V853" "V854" "V855"
## [856] "V856" "V857" "V858" "V859" "V860" "V861" "V862" "V863" "V864"
## [865] "V865" "V866" "V867" "V868" "V869" "V870" "V871" "V872" "V873"
## [874] "V874" "V875" "V876" "V877" "V878" "V879" "V880" "V881" "V882"
## [883] "V883" "V884" "V885" "V886" "V887" "V888" "V889" "V890" "V891"
## [892] "V892" "V893" "V894" "V895" "V896" "V897" "V898" "V899" "V900"
## [901] "V901" "V902" "V903" "V904" "V905" "V906" "V907" "V908" "V909"
## [910] "V910" "V911" "V912" "V913" "V914" "V915" "V916" "V917" "V918"
## [919] "V919" "V920" "V921" "V922" "V923" "V924" "V925" "V926" "V927"
## [928] "V928" "V929" "V930" "V931" "V932" "V933" "V934" "V935" "V936"
## [937] "V937" "V938" "V939" "V940" "V941" "V942" "V943" "V944" "V945"
## [946] "V946" "V947" "V948" "V949" "V950" "V951" "V952" "V953" "V954"
## [955] "V955" "V956" "V957" "V958" "V959" "V960" "V961" "V962" "V963"
## [964] "V964" "V965" "V966" "V967" "V968" "V969" "V970" "V971" "V972"
## [973] "V973" "V974" "V975" "V976" "V977" "V978" "V979" "V980" "V981"
## [982] "V982" "V983" "V984" "V985" "V986" "V987" "V988" "V989" "V990"
## [991] "V991" "V992" "V993" "V994" "V995" "V996" "V997" "V998" "V999"
## [1000] "V1000" "V1001" "V1002" "V1003" "V1004" "V1005" "V1006" "V1007" "V1008"
## [1009] "V1009" "V1010" "V1011" "V1012" "V1013" "V1014" "V1015" "V1016" "V1017"
## [1018] "V1018" "V1019" "V1020" "V1021" "V1022" "V1023" "V1024" "V1025" "V1026"
## [1027] "V1027" "V1028" "V1029" "V1030" "V1031" "V1032" "V1033" "V1034" "V1035"
## [1036] "V1036" "V1037" "V1038" "V1039" "V1040" "V1041" "V1042" "V1043" "V1044"
## [1045] "V1045" "V1046" "V1047" "V1048" "V1049" "V1050" "V1051" "V1052" "V1053"
## [1054] "V1054" "V1055" "V1056" "V1057" "V1058" "V1059" "V1060" "V1061" "V1062"
## [1063] "V1063" "V1064" "V1065" "V1066" "V1067" "V1068" "V1069" "V1070" "V1071"
## [1072] "V1072" "V1073" "V1074" "V1075" "V1076" "V1077" "V1078" "V1079" "V1080"
## [1081] "V1081" "V1082" "V1083" "V1084" "V1085" "V1086" "V1087" "V1088" "V1089"
## [1090] "V1090" "V1091" "V1092" "V1093" "V1094" "V1095" "V1096" "V1097" "V1098"
## [1099] "V1099" "V1100" "V1101" "V1102" "V1103" "V1104" "V1105" "V1106" "V1107"
## [1108] "V1108" "V1109" "V1110" "V1111" "V1112" "V1113" "V1114" "V1115" "V1116"
## [1117] "V1117" "V1118" "V1119" "V1120" "V1121" "V1122" "V1123" "V1124" "V1125"
## [1126] "V1126" "V1127" "V1128" "V1129" "V1130" "V1131" "V1132" "V1133" "V1134"
## [1135] "V1135" "V1136" "V1137" "V1138" "V1139" "V1140" "V1141" "V1142" "V1143"
## [1144] "V1144" "V1145" "V1146" "V1147" "V1148" "V1149" "V1150" "V1151" "V1152"
## [1153] "V1153" "V1154" "V1155" "V1156" "V1157" "V1158" "V1159" "V1160" "V1161"
## [1162] "V1162" "V1163" "V1164" "V1165" "V1166" "V1167" "V1168" "V1169" "V1170"
## [1171] "V1171" "V1172" "V1173" "V1174" "V1175" "V1176" "V1177" "V1178" "V1179"
## [1180] "V1180" "V1181" "V1182" "V1183" "V1184" "V1185" "V1186" "V1187" "V1188"
## [1189] "V1189" "V1190" "V1191" "V1192" "V1193" "V1194" "V1195" "V1196" "V1197"
## [1198] "V1198" "V1199" "V1200" "V1201" "V1202" "V1203" "V1204" "V1205" "V1206"
## [1207] "V1207" "V1208" "V1209" "V1210" "V1211" "V1212" "V1213" "V1214" "V1215"
## [1216] "V1216" "V1217" "V1218" "V1219" "V1220" "V1221" "V1222" "V1223" "V1224"
## [1225] "V1225" "V1226" "V1227" "V1228" "V1229" "V1230" "V1231" "V1232" "V1233"
## [1234] "V1234" "V1235" "V1236" "V1237" "V1238" "V1239" "V1240" "V1241" "V1242"
## [1243] "V1243" "V1244" "V1245" "V1246" "V1247" "V1248" "V1249" "V1250" "V1251"
## [1252] "V1252" "V1253" "V1254" "V1255" "V1256" "V1257" "V1258" "V1259" "V1260"
## [1261] "V1261" "V1262" "V1263" "V1264" "V1265" "V1266" "V1267" "V1268" "V1269"
## [1270] "V1270" "V1271" "V1272" "V1273" "V1274" "V1275" "V1276" "V1277" "V1278"
## [1279] "V1279" "V1280" "V1281" "V1282" "V1283" "V1284" "V1285" "V1286" "V1287"
## [1288] "V1288" "V1289" "V1290" "V1291" "V1292" "V1293" "V1294" "V1295" "V1296"
## [1297] "V1297" "V1298" "V1299" "V1300" "V1301" "V1302" "V1303" "V1304" "V1305"
## [1306] "V1306" "V1307" "V1308" "V1309" "V1310" "V1311" "V1312" "V1313" "V1314"
## [1315] "V1315" "V1316" "V1317" "V1318" "V1319" "V1320" "V1321" "V1322" "V1323"
## [1324] "V1324" "V1325" "V1326" "V1327" "V1328" "V1329" "V1330" "V1331" "V1332"
## [1333] "V1333" "V1334" "V1335" "V1336" "V1337" "V1338" "V1339" "V1340" "V1341"
## [1342] "V1342" "V1343" "V1344" "V1345" "V1346" "V1347" "V1348" "V1349" "V1350"
## [1351] "V1351" "V1352" "V1353" "V1354" "V1355" "V1356" "V1357" "V1358" "V1359"
## [1360] "V1360" "V1361" "V1362" "V1363" "V1364" "V1365" "V1366" "V1367" "V1368"
## [1369] "V1369" "V1370" "V1371" "V1372" "V1373" "V1374" "V1375" "V1376" "V1377"
## [1378] "V1378" "V1379" "V1380" "V1381" "V1382" "V1383" "V1384" "V1385" "V1386"
## [1387] "V1387" "V1388" "V1389" "V1390" "V1391" "V1392" "V1393" "V1394" "V1395"
## [1396] "V1396" "V1397" "V1398" "V1399" "V1400" "V1401" "V1402" "V1403" "V1404"
## [1405] "V1405" "V1406" "V1407" "V1408" "V1409" "V1410" "V1411" "V1412" "V1413"
## [1414] "V1414" "V1415" "V1416" "V1417" "V1418" "V1419" "V1420" "V1421" "V1422"
## [1423] "V1423" "V1424" "V1425" "V1426" "V1427" "V1428" "V1429" "V1430" "V1431"
## [1432] "V1432" "V1433" "V1434" "V1435" "V1436" "V1437" "V1438" "V1439" "V1440"
## [1441] "V1441" "V1442" "V1443" "V1444" "V1445" "V1446" "V1447" "V1448" "V1449"
## [1450] "V1450" "V1451" "V1452" "V1453" "V1454" "V1455" "V1456" "V1457" "V1458"
## [1459] "V1459" "V1460" "V1461" "V1462" "V1463" "V1464" "V1465" "V1466" "V1467"
## [1468] "V1468" "V1469" "V1470" "V1471" "V1472" "V1473" "V1474" "V1475" "V1476"
## [1477] "V1477" "V1478" "V1479" "V1480" "V1481" "V1482" "V1483" "V1484" "V1485"
## [1486] "V1486" "V1487" "V1488" "V1489" "V1490" "V1491" "V1492" "V1493" "V1494"
## [1495] "V1495" "V1496" "V1497" "V1498" "V1499" "V1500" "V1501" "V1502" "V1503"
## [1504] "V1504" "V1505" "V1506" "V1507" "V1508" "V1509" "V1510" "V1511" "V1512"
## [1513] "V1513" "V1514" "V1515" "V1516" "V1517" "V1518" "V1519" "V1520" "V1521"
## [1522] "V1522" "V1523" "V1524" "V1525" "V1526" "V1527" "V1528" "V1529" "V1530"
## [1531] "V1531" "V1532" "V1533" "V1534" "V1535" "V1536" "V1537" "V1538" "V1539"
## [1540] "V1540" "V1541" "V1542" "V1543" "V1544" "V1545" "V1546" "V1547" "V1548"
## [1549] "V1549" "V1550" "V1551" "V1552" "V1553" "V1554" "V1555" "V1556" "V1557"
## [1558] "V1558" "V1559" "V1560" "V1561" "V1562" "V1563" "V1564" "V1565" "V1566"
## [1567] "V1567" "V1568" "V1569" "V1570" "V1571" "V1572" "V1573" "V1574" "V1575"
## [1576] "V1576" "V1577" "V1578" "V1579" "V1580" "V1581" "V1582" "V1583" "V1584"
## [1585] "V1585" "V1586" "V1587" "V1588" "V1589" "V1590" "V1591" "V1592" "V1593"
## [1594] "V1594" "V1595" "V1596" "V1597" "V1598" "V1599" "V1600" "V1601" "V1602"
## [1603] "V1603" "V1604" "V1605" "V1606" "V1607" "V1608" "V1609" "V1610" "V1611"
## [1612] "V1612" "V1613" "V1614" "V1615" "V1616" "V1617" "V1618" "V1619" "V1620"
## [1621] "V1621" "V1622" "V1623" "V1624" "V1625" "V1626" "V1627" "V1628" "V1629"
## [1630] "V1630" "V1631" "V1632" "V1633" "V1634" "V1635" "V1636" "V1637" "V1638"
## [1639] "V1639" "V1640" "V1641" "V1642" "V1643" "V1644" "V1645" "V1646" "V1647"
## [1648] "V1648" "V1649" "V1650" "V1651" "V1652" "V1653" "V1654" "V1655" "V1656"
## [1657] "V1657" "V1658" "V1659" "V1660" "V1661" "V1662" "V1663" "V1664" "V1665"
## [1666] "V1666" "V1667" "V1668" "V1669" "V1670" "V1671" "V1672" "V1673" "V1674"
## [1675] "V1675" "V1676" "V1677" "V1678" "V1679" "V1680" "V1681" "V1682" "V1683"
## [1684] "V1684" "V1685" "V1686" "V1687" "V1688" "V1689" "V1690" "V1691" "V1692"
## [1693] "V1693" "V1694" "V1695" "V1696" "V1697" "V1698" "V1699" "V1700" "V1701"
## [1702] "V1702" "V1703" "V1704" "V1705" "V1706" "V1707" "V1708" "V1709" "V1710"
## [1711] "V1711" "V1712" "V1713" "V1714" "V1715" "V1716" "V1717" "V1718" "V1719"
## [1720] "V1720" "V1721" "V1722" "V1723" "V1724" "V1725" "V1726" "V1727" "V1728"
## [1729] "V1729" "V1730" "V1731" "V1732" "V1733" "V1734" "V1735" "V1736" "V1737"
## [1738] "V1738" "V1739" "V1740" "V1741" "V1742" "V1743" "V1744" "V1745" "V1746"
## [1747] "V1747" "V1748" "V1749" "V1750" "V1751" "V1752" "V1753" "V1754" "V1755"
## [1756] "V1756" "V1757" "V1758" "V1759" "V1760" "V1761" "V1762" "V1763" "V1764"
## [1765] "V1765" "V1766" "V1767" "V1768" "V1769" "V1770" "V1771" "V1772" "V1773"
## [1774] "V1774" "V1775" "V1776" "V1777" "V1778" "V1779" "V1780" "V1781" "V1782"
## [1783] "V1783" "V1784" "V1785" "V1786" "V1787" "V1788" "V1789" "V1790" "V1791"
## [1792] "V1792" "V1793" "V1794" "V1795" "V1796" "V1797" "V1798" "V1799" "V1800"
## [1801] "V1801" "V1802" "V1803" "V1804" "V1805" "V1806" "V1807" "V1808" "V1809"
## [1810] "V1810" "V1811" "V1812" "V1813" "V1814" "V1815" "V1816" "V1817" "V1818"
## [1819] "V1819" "V1820" "V1821" "V1822" "V1823" "V1824" "V1825" "V1826" "V1827"
## [1828] "V1828" "V1829" "V1830" "V1831" "V1832" "V1833" "V1834" "V1835" "V1836"
## [1837] "V1837" "V1838" "V1839" "V1840" "V1841" "V1842" "V1843" "V1844" "V1845"
## [1846] "V1846" "V1847" "V1848" "V1849" "V1850" "V1851" "V1852" "V1853" "V1854"
## [1855] "V1855" "V1856" "V1857" "V1858" "V1859" "V1860" "V1861" "V1862" "V1863"
## [1864] "V1864" "V1865" "V1866" "V1867" "V1868" "V1869" "V1870" "V1871" "V1872"
## [1873] "V1873" "V1874" "V1875" "V1876" "V1877" "V1878" "V1879" "V1880" "V1881"
## [1882] "V1882" "V1883" "V1884" "V1885" "V1886" "V1887" "V1888" "V1889" "V1890"
## [1891] "V1891" "V1892" "V1893" "V1894" "V1895" "V1896" "V1897" "V1898" "V1899"
## [1900] "V1900" "V1901" "V1902" "V1903" "V1904" "V1905" "V1906" "V1907" "V1908"
## [1909] "V1909" "V1910" "V1911" "V1912" "V1913" "V1914" "V1915" "V1916" "V1917"
## [1918] "V1918" "V1919" "V1920" "V1921" "V1922" "V1923" "V1924" "V1925" "V1926"
## [1927] "V1927" "V1928" "V1929" "V1930" "V1931" "V1932" "V1933" "V1934" "V1935"
## [1936] "V1936" "V1937" "V1938" "V1939" "V1940" "V1941" "V1942" "V1943" "V1944"
## [1945] "V1945" "V1946" "V1947" "V1948" "V1949" "V1950" "V1951" "V1952" "V1953"
## [1954] "V1954" "V1955" "V1956" "V1957" "V1958" "V1959" "V1960" "V1961" "V1962"
## [1963] "V1963" "V1964" "V1965" "V1966" "V1967" "V1968" "V1969" "V1970" "V1971"
## [1972] "V1972" "V1973" "V1974" "V1975" "V1976" "V1977" "V1978" "V1979" "V1980"
## [1981] "V1981" "V1982" "V1983" "V1984" "V1985" "V1986" "V1987" "V1988" "V1989"
## [1990] "V1990" "V1991" "V1992" "V1993" "V1994" "V1995" "V1996" "V1997" "V1998"
## [1999] "V1999" "V2000" "V2001" "V2002" "V2003" "V2004" "V2005" "V2006" "V2007"
## [2008] "V2008" "V2009" "V2010" "V2011" "V2012" "V2013" "V2014" "V2015" "V2016"
## [2017] "V2017" "V2018" "V2019" "V2020" "V2021" "V2022" "V2023" "V2024" "V2025"
## [2026] "V2026" "V2027" "V2028" "V2029" "V2030" "V2031" "V2032" "V2033" "V2034"
## [2035] "V2035" "V2036" "V2037" "V2038" "V2039" "V2040" "V2041" "V2042" "V2043"
## [2044] "V2044" "V2045" "V2046" "V2047" "V2048" "V2049" "V2050" "V2051" "V2052"
## [2053] "V2053" "V2054" "V2055" "V2056" "V2057" "V2058" "V2059" "V2060" "V2061"
## [2062] "V2062" "V2063" "V2064" "V2065" "V2066" "V2067" "V2068" "V2069" "V2070"
## [2071] "V2071" "V2072" "V2073" "V2074" "V2075" "V2076" "V2077" "V2078" "V2079"
## [2080] "V2080" "V2081" "V2082" "V2083" "V2084" "V2085" "V2086" "V2087" "V2088"
## [2089] "V2089" "V2090" "V2091" "V2092" "V2093" "V2094" "V2095" "V2096" "V2097"
## [2098] "V2098" "V2099" "V2100" "V2101" "V2102" "V2103" "V2104" "V2105" "V2106"
## [2107] "V2107" "V2108" "V2109" "V2110" "V2111" "V2112" "V2113" "V2114" "V2115"
## [2116] "V2116" "V2117" "V2118" "V2119" "V2120" "V2121" "V2122" "V2123" "V2124"
## [2125] "V2125" "V2126" "V2127" "V2128" "V2129" "V2130" "V2131" "V2132" "V2133"
## [2134] "V2134" "V2135" "V2136" "V2137" "V2138" "V2139" "V2140" "V2141" "V2142"
## [2143] "V2143" "V2144" "V2145" "V2146" "V2147" "V2148" "V2149" "V2150" "V2151"
## [2152] "V2152" "V2153" "V2154" "V2155" "V2156" "V2157" "V2158" "V2159" "V2160"
## [2161] "V2161" "V2162" "V2163" "V2164" "V2165" "V2166" "V2167" "V2168" "V2169"
## [2170] "V2170" "V2171" "V2172" "V2173" "V2174" "V2175" "V2176" "V2177" "V2178"
## [2179] "V2179" "V2180" "V2181" "V2182" "V2183" "V2184" "V2185" "V2186" "V2187"
## [2188] "V2188" "V2189" "V2190" "V2191" "V2192" "V2193" "V2194" "V2195" "V2196"
## [2197] "V2197" "V2198" "V2199" "V2200" "V2201" "V2202" "V2203" "V2204" "V2205"
## [2206] "V2206" "V2207" "V2208" "V2209" "V2210" "V2211" "V2212" "V2213" "V2214"
## [2215] "V2215" "V2216" "V2217" "V2218" "V2219" "V2220" "V2221" "V2222" "V2223"
## [2224] "V2224" "V2225" "V2226" "V2227" "V2228" "V2229" "V2230" "V2231" "V2232"
## [2233] "V2233" "V2234" "V2235" "V2236" "V2237" "V2238" "V2239" "V2240" "V2241"
## [2242] "V2242" "V2243" "V2244" "V2245" "V2246" "V2247" "V2248" "V2249" "V2250"
## [2251] "V2251" "V2252" "V2253" "V2254" "V2255" "V2256" "V2257" "V2258" "V2259"
## [2260] "V2260" "V2261" "V2262" "V2263" "V2264" "V2265" "V2266" "V2267" "V2268"
## [2269] "V2269" "V2270" "V2271" "V2272" "V2273" "V2274" "V2275" "V2276" "V2277"
## [2278] "V2278" "V2279" "V2280" "V2281" "V2282" "V2283" "V2284" "V2285" "V2286"
## [2287] "V2287" "V2288" "V2289" "V2290" "V2291" "V2292" "V2293" "V2294" "V2295"
## [2296] "V2296" "V2297" "V2298" "V2299" "V2300" "V2301" "V2302" "V2303" "V2304"
## [2305] "V2305" "V2306" "V2307" "V2308" "V2309" "V2310" "V2311" "V2312" "V2313"
## [2314] "V2314" "V2315" "V2316" "V2317" "V2318" "V2319" "V2320" "V2321" "V2322"
## [2323] "V2323" "V2324" "V2325" "V2326" "V2327" "V2328" "V2329" "V2330" "V2331"
## [2332] "V2332" "V2333" "V2334" "V2335" "V2336" "V2337" "V2338" "V2339" "V2340"
## [2341] "V2341" "V2342" "V2343" "V2344" "V2345" "V2346" "V2347" "V2348" "V2349"
## [2350] "V2350" "V2351" "V2352" "V2353" "V2354" "V2355" "V2356" "V2357" "V2358"
## [2359] "V2359" "V2360" "V2361" "V2362" "V2363" "V2364" "V2365" "V2366" "V2367"
## [2368] "V2368" "V2369" "V2370" "V2371" "V2372" "V2373" "V2374" "V2375" "V2376"
## [2377] "V2377" "V2378" "V2379" "V2380" "V2381" "V2382" "V2383" "V2384" "V2385"
## [2386] "V2386" "V2387" "V2388" "V2389" "V2390" "V2391" "V2392" "V2393" "V2394"
## [2395] "V2395" "V2396" "V2397" "V2398" "V2399" "V2400" "V2401" "V2402" "V2403"
## [2404] "V2404" "V2405" "V2406" "V2407" "V2408" "V2409" "V2410" "V2411" "V2412"
## [2413] "V2413" "V2414" "V2415" "V2416" "V2417" "V2418" "V2419" "V2420" "V2421"
## [2422] "V2422" "V2423" "V2424" "V2425" "V2426" "V2427" "V2428" "V2429" "V2430"
## [2431] "V2431" "V2432" "V2433" "V2434" "V2435" "V2436" "V2437" "V2438" "V2439"
## [2440] "V2440" "V2441" "V2442" "V2443" "V2444" "V2445" "V2446" "V2447" "V2448"
## [2449] "V2449" "V2450" "V2451" "V2452" "V2453" "V2454" "V2455" "V2456" "V2457"
## [2458] "V2458" "V2459" "V2460" "V2461" "V2462" "V2463" "V2464" "V2465" "V2466"
## [2467] "V2467" "V2468" "V2469" "V2470" "V2471" "V2472" "V2473" "V2474" "V2475"
## [2476] "V2476" "V2477" "V2478" "V2479" "V2480" "V2481" "V2482" "V2483" "V2484"
## [2485] "V2485" "V2486" "V2487" "V2488" "V2489" "V2490" "V2491" "V2492" "V2493"
## [2494] "V2494" "V2495" "V2496" "V2497" "V2498" "V2499" "V2500" "V2501" "V2502"
## [2503] "V2503" "V2504" "V2505" "V2506" "V2507" "V2508" "V2509" "V2510" "V2511"
## [2512] "V2512" "V2513" "V2514" "V2515" "V2516" "V2517" "V2518" "V2519" "V2520"
## [2521] "V2521" "V2522" "V2523" "V2524" "V2525" "V2526" "V2527" "V2528" "V2529"
## [2530] "V2530" "V2531" "V2532" "V2533" "V2534" "V2535" "V2536" "V2537" "V2538"
## [2539] "V2539" "V2540" "V2541" "V2542" "V2543" "V2544" "V2545" "V2546" "V2547"
## [2548] "V2548" "V2549" "V2550" "V2551" "V2552" "V2553" "V2554" "V2555" "V2556"
## [2557] "V2557" "V2558" "V2559" "V2560" "V2561" "V2562" "V2563" "V2564" "V2565"
## [2566] "V2566" "V2567" "V2568" "V2569" "V2570" "V2571" "V2572" "V2573" "V2574"
## [2575] "V2575" "V2576" "V2577" "V2578" "V2579" "V2580" "V2581" "V2582" "V2583"
## [2584] "V2584" "V2585" "V2586" "V2587" "V2588" "V2589" "V2590" "V2591" "V2592"
## [2593] "V2593" "V2594" "V2595" "V2596" "V2597" "V2598" "V2599" "V2600" "V2601"
## [2602] "V2602" "V2603" "V2604" "V2605" "V2606" "V2607" "V2608" "V2609" "V2610"
## [2611] "V2611" "V2612" "V2613" "V2614" "V2615" "V2616" "V2617" "V2618" "V2619"
## [2620] "V2620" "V2621" "V2622" "V2623" "V2624" "V2625" "V2626" "V2627" "V2628"
## [2629] "V2629" "V2630" "V2631" "V2632" "V2633" "V2634" "V2635" "V2636" "V2637"
## [2638] "V2638" "V2639" "V2640" "V2641" "V2642" "V2643" "V2644" "V2645" "V2646"
## [2647] "V2647" "V2648" "V2649" "V2650" "V2651" "V2652" "V2653" "V2654" "V2655"
## [2656] "V2656" "V2657" "V2658" "V2659" "V2660" "V2661" "V2662" "V2663" "V2664"
## [2665] "V2665" "V2666" "V2667" "V2668" "V2669" "V2670" "V2671" "V2672" "V2673"
## [2674] "V2674" "V2675" "V2676" "V2677" "V2678" "V2679" "V2680" "V2681" "V2682"
## [2683] "V2683" "V2684" "V2685" "V2686" "V2687" "V2688" "V2689" "V2690" "V2691"
## [2692] "V2692" "V2693" "V2694" "V2695" "V2696" "V2697" "V2698" "V2699" "V2700"
## [2701] "V2701" "V2702" "V2703" "V2704" "V2705" "V2706" "V2707" "V2708" "V2709"
## [2710] "V2710" "V2711" "V2712" "V2713" "V2714" "V2715" "V2716" "V2717" "V2718"
## [2719] "V2719" "V2720" "V2721" "V2722" "V2723" "V2724" "V2725" "V2726" "V2727"
## [2728] "V2728" "V2729" "V2730" "V2731" "V2732" "V2733" "V2734" "V2735" "V2736"
## [2737] "V2737" "V2738" "V2739" "V2740" "V2741" "V2742" "V2743" "V2744" "V2745"
## [2746] "V2746" "V2747" "V2748" "V2749" "V2750" "V2751" "V2752" "V2753" "V2754"
## [2755] "V2755" "V2756" "V2757" "V2758" "V2759" "V2760" "V2761" "V2762" "V2763"
## [2764] "V2764" "V2765" "V2766" "V2767" "V2768" "V2769" "V2770" "V2771" "V2772"
## [2773] "V2773" "V2774" "V2775" "V2776" "V2777" "V2778" "V2779" "V2780" "V2781"
## [2782] "V2782" "V2783" "V2784" "V2785" "V2786" "V2787" "V2788" "V2789" "V2790"
## [2791] "V2791" "V2792" "V2793" "V2794" "V2795" "V2796" "V2797" "V2798" "V2799"
## [2800] "V2800" "V2801" "V2802" "V2803" "V2804" "V2805" "V2806" "V2807" "V2808"
## [2809] "V2809" "V2810" "V2811" "V2812" "V2813" "V2814" "V2815" "V2816" "V2817"
## [2818] "V2818" "V2819" "V2820" "V2821" "V2822" "V2823" "V2824" "V2825" "V2826"
## [2827] "V2827" "V2828" "V2829" "V2830" "V2831" "V2832" "V2833" "V2834" "V2835"
## [2836] "V2836" "V2837" "V2838" "V2839" "V2840" "V2841" "V2842" "V2843" "V2844"
## [2845] "V2845" "V2846" "V2847" "V2848" "V2849" "V2850" "V2851" "V2852" "V2853"
## [2854] "V2854" "V2855" "V2856" "V2857" "V2858" "V2859" "V2860" "V2861" "V2862"
## [2863] "V2863" "V2864" "V2865" "V2866" "V2867" "V2868" "V2869" "V2870" "V2871"
## [2872] "V2872" "V2873" "V2874" "V2875" "V2876" "V2877" "V2878" "V2879" "V2880"
## [2881] "V2881" "V2882" "V2883" "V2884" "V2885" "V2886" "V2887" "V2888" "V2889"
## [2890] "V2890" "V2891" "V2892" "V2893" "V2894" "V2895" "V2896" "V2897" "V2898"
## [2899] "V2899" "V2900" "V2901" "V2902" "V2903" "V2904" "V2905" "V2906" "V2907"
## [2908] "V2908" "V2909" "V2910" "V2911" "V2912" "V2913" "V2914" "V2915" "V2916"
## [2917] "V2917" "V2918" "V2919" "V2920" "V2921" "V2922" "V2923" "V2924" "V2925"
## [2926] "V2926" "V2927" "V2928" "V2929" "V2930" "V2931" "V2932" "V2933" "V2934"
## [2935] "V2935" "V2936" "V2937" "V2938" "V2939" "V2940" "V2941" "V2942" "V2943"
## [2944] "V2944" "V2945" "V2946" "V2947" "V2948" "V2949" "V2950" "V2951" "V2952"
## [2953] "V2953" "V2954" "V2955" "V2956" "V2957" "V2958" "V2959" "V2960" "V2961"
## [2962] "V2962" "V2963" "V2964" "V2965" "V2966" "V2967" "V2968" "V2969" "V2970"
## [2971] "V2971" "V2972" "V2973" "V2974" "V2975" "V2976" "V2977" "V2978" "V2979"
## [2980] "V2980" "V2981" "V2982" "V2983" "V2984" "V2985" "V2986" "V2987" "V2988"
## [2989] "V2989" "V2990" "V2991" "V2992" "V2993" "V2994" "V2995" "V2996" "V2997"
## [2998] "V2998" "V2999" "V3000" "V3001" "V3002" "V3003" "V3004" "V3005" "V3006"
## [3007] "V3007" "V3008" "V3009" "V3010" "V3011" "V3012" "V3013" "V3014" "V3015"
## [3016] "V3016" "V3017" "V3018" "V3019" "V3020" "V3021" "V3022" "V3023" "V3024"
## [3025] "V3025" "V3026" "V3027" "V3028" "V3029" "V3030" "V3031" "V3032" "V3033"
## [3034] "V3034" "V3035" "V3036" "V3037" "V3038" "V3039" "V3040" "V3041" "V3042"
## [3043] "V3043" "V3044" "V3045" "V3046" "V3047" "V3048" "V3049" "V3050" "V3051"
## [3052] "V3052" "V3053" "V3054" "V3055" "V3056" "V3057" "V3058" "V3059" "V3060"
## [3061] "V3061" "V3062" "V3063" "V3064" "V3065" "V3066" "V3067" "V3068" "V3069"
## [3070] "V3070" "V3071" "V3072" "V3073" "V3074" "V3075" "V3076" "V3077" "V3078"
## [3079] "V3079" "V3080" "V3081" "V3082" "V3083" "V3084" "V3085" "V3086" "V3087"
## [3088] "V3088" "V3089" "V3090" "V3091" "V3092" "V3093" "V3094" "V3095" "V3096"
## [3097] "V3097" "V3098" "V3099" "V3100" "V3101" "V3102" "V3103" "V3104" "V3105"
## [3106] "V3106" "V3107" "V3108" "V3109" "V3110" "V3111" "V3112" "V3113" "V3114"
## [3115] "V3115" "V3116" "V3117" "V3118" "V3119" "V3120" "V3121" "V3122" "V3123"
## [3124] "V3124" "V3125" "V3126" "V3127" "V3128" "V3129" "V3130" "V3131" "V3132"
## [3133] "V3133" "V3134" "V3135" "V3136" "V3137" "V3138" "V3139" "V3140" "V3141"
## [3142] "V3142" "V3143" "V3144" "V3145" "V3146" "V3147" "V3148" "V3149" "V3150"
## [3151] "V3151" "V3152" "V3153" "V3154" "V3155" "V3156" "V3157" "V3158" "V3159"
## [3160] "V3160" "V3161" "V3162" "V3163" "V3164" "V3165" "V3166" "V3167" "V3168"
## [3169] "V3169" "V3170" "V3171" "V3172" "V3173" "V3174" "V3175" "V3176" "V3177"
## [3178] "V3178" "V3179" "V3180" "V3181" "V3182" "V3183" "V3184" "V3185" "V3186"
## [3187] "V3187" "V3188" "V3189" "V3190" "V3191" "V3192" "V3193" "V3194" "V3195"
## [3196] "V3196" "V3197" "V3198" "V3199" "V3200" "V3201" "V3202" "V3203" "V3204"
## [3205] "V3205" "V3206" "V3207" "V3208" "V3209" "V3210" "V3211" "V3212" "V3213"
## [3214] "V3214" "V3215" "V3216" "V3217" "V3218" "V3219" "V3220" "V3221" "V3222"
## [3223] "V3223" "V3224" "V3225" "V3226" "V3227" "V3228" "V3229" "V3230" "V3231"
## [3232] "V3232" "V3233" "V3234" "V3235" "V3236" "V3237" "V3238" "V3239" "V3240"
## [3241] "V3241" "V3242" "V3243" "V3244" "V3245" "V3246" "V3247" "V3248" "V3249"
## [3250] "V3250" "V3251" "V3252" "V3253" "V3254" "V3255" "V3256" "V3257" "V3258"
## [3259] "V3259" "V3260" "V3261" "V3262" "V3263" "V3264" "V3265" "V3266" "V3267"
## [3268] "V3268" "V3269" "V3270" "V3271" "V3272" "V3273" "V3274" "V3275" "V3276"
## [3277] "V3277" "V3278" "V3279" "V3280" "V3281" "V3282" "V3283" "V3284" "V3285"
## [3286] "V3286" "V3287" "V3288" "V3289" "V3290" "V3291" "V3292" "V3293" "V3294"
## [3295] "V3295" "V3296" "V3297" "V3298" "V3299" "V3300" "V3301" "V3302" "V3303"
## [3304] "V3304" "V3305" "V3306" "V3307" "V3308" "V3309" "V3310" "V3311" "V3312"
## [3313] "V3313" "V3314" "V3315" "V3316" "V3317" "V3318" "V3319" "V3320" "V3321"
## [3322] "V3322" "V3323" "V3324" "V3325" "V3326" "V3327" "V3328" "V3329" "V3330"
## [3331] "V3331" "V3332" "V3333" "V3334" "V3335" "V3336" "V3337" "V3338" "V3339"
## [3340] "V3340" "V3341" "V3342" "V3343" "V3344" "V3345" "V3346" "V3347" "V3348"
## [3349] "V3349" "V3350" "V3351" "V3352" "V3353" "V3354" "V3355" "V3356" "V3357"
## [3358] "V3358" "V3359" "V3360" "V3361" "V3362" "V3363" "V3364" "V3365" "V3366"
## [3367] "V3367" "V3368" "V3369" "V3370" "V3371" "V3372" "V3373" "V3374" "V3375"
## [3376] "V3376" "V3377" "V3378" "V3379" "V3380" "V3381" "V3382" "V3383" "V3384"
## [3385] "V3385" "V3386" "V3387" "V3388" "V3389" "V3390" "V3391" "V3392" "V3393"
## [3394] "V3394" "V3395" "V3396" "V3397" "V3398" "V3399" "V3400" "V3401" "V3402"
## [3403] "V3403" "V3404" "V3405" "V3406" "V3407" "V3408" "V3409" "V3410" "V3411"
## [3412] "V3412" "V3413" "V3414" "V3415" "V3416" "V3417" "V3418" "V3419" "V3420"
## [3421] "V3421" "V3422" "V3423" "V3424" "V3425" "V3426" "V3427" "V3428" "V3429"
## [3430] "V3430" "V3431" "V3432" "V3433" "V3434" "V3435" "V3436" "V3437" "V3438"
## [3439] "V3439" "V3440" "V3441" "V3442" "V3443" "V3444" "V3445" "V3446" "V3447"
## [3448] "V3448" "V3449" "V3450" "V3451" "V3452" "V3453" "V3454" "V3455" "V3456"
## [3457] "V3457" "V3458" "V3459" "V3460" "V3461" "V3462" "V3463" "V3464" "V3465"
## [3466] "V3466" "V3467" "V3468" "V3469" "V3470" "V3471" "V3472" "V3473" "V3474"
## [3475] "V3475" "V3476" "V3477" "V3478" "V3479" "V3480" "V3481" "V3482" "V3483"
## [3484] "V3484" "V3485" "V3486" "V3487" "V3488" "V3489" "V3490" "V3491" "V3492"
## [3493] "V3493" "V3494" "V3495" "V3496" "V3497" "V3498" "V3499" "V3500" "V3501"
## [3502] "V3502" "V3503" "V3504" "V3505" "V3506" "V3507" "V3508" "V3509" "V3510"
## [3511] "V3511" "V3512" "V3513" "V3514" "V3515" "V3516" "V3517" "V3518" "V3519"
## [3520] "V3520" "V3521" "V3522" "V3523" "V3524" "V3525" "V3526" "V3527" "V3528"
## [3529] "V3529" "V3530" "V3531" "V3532" "V3533" "V3534" "V3535" "V3536" "V3537"
## [3538] "V3538" "V3539" "V3540" "V3541" "V3542" "V3543" "V3544" "V3545" "V3546"
## [3547] "V3547" "V3548" "V3549" "V3550" "V3551" "V3552" "V3553" "V3554" "V3555"
## [3556] "V3556" "V3557" "V3558" "V3559" "V3560" "V3561" "V3562" "V3563" "V3564"
## [3565] "V3565" "V3566" "V3567" "V3568" "V3569" "V3570" "V3571" "V3572" "V3573"
## [3574] "V3574" "V3575" "V3576" "V3577" "V3578" "V3579" "V3580" "V3581" "V3582"
## [3583] "V3583" "V3584" "V3585" "V3586" "V3587" "V3588" "V3589" "V3590" "V3591"
## [3592] "V3592" "V3593" "V3594" "V3595" "V3596" "V3597" "V3598" "V3599" "V3600"
## [3601] "V3601" "V3602" "V3603" "V3604" "V3605" "V3606" "V3607" "V3608" "V3609"
## [3610] "V3610" "V3611" "V3612" "V3613" "V3614" "V3615" "V3616" "V3617" "V3618"
## [3619] "V3619" "V3620" "V3621" "V3622" "V3623" "V3624" "V3625" "V3626" "V3627"
## [3628] "V3628" "V3629" "V3630" "V3631" "V3632" "V3633" "V3634" "V3635" "V3636"
## [3637] "V3637" "V3638" "V3639" "V3640" "V3641" "V3642" "V3643" "V3644" "V3645"
## [3646] "V3646" "V3647" "V3648" "V3649" "V3650" "V3651" "V3652" "V3653" "V3654"
## [3655] "V3655" "V3656" "V3657" "V3658" "V3659" "V3660" "V3661" "V3662" "V3663"
## [3664] "V3664" "V3665" "V3666" "V3667" "V3668" "V3669" "V3670" "V3671" "V3672"
## [3673] "V3673" "V3674" "V3675" "V3676" "V3677" "V3678" "V3679" "V3680" "V3681"
## [3682] "V3682" "V3683" "V3684" "V3685" "V3686" "V3687" "V3688" "V3689" "V3690"
## [3691] "V3691" "V3692" "V3693" "V3694" "V3695" "V3696" "V3697" "V3698" "V3699"
## [3700] "V3700" "V3701" "V3702" "V3703" "V3704" "V3705" "V3706" "V3707" "V3708"
## [3709] "V3709" "V3710" "V3711" "V3712" "V3713" "V3714" "V3715" "V3716" "V3717"
## [3718] "V3718" "V3719" "V3720" "V3721" "V3722" "V3723" "V3724" "V3725" "V3726"
## [3727] "V3727" "V3728" "V3729" "V3730" "V3731" "V3732" "V3733" "V3734" "V3735"
## [3736] "V3736" "V3737" "V3738" "V3739" "V3740" "V3741" "V3742" "V3743" "V3744"
## [3745] "V3745" "V3746" "V3747" "V3748" "V3749" "V3750" "V3751" "V3752" "V3753"
## [3754] "V3754" "V3755" "V3756" "V3757" "V3758" "V3759" "V3760" "V3761" "V3762"
## [3763] "V3763" "V3764" "V3765" "V3766" "V3767" "V3768" "V3769" "V3770" "V3771"
## [3772] "V3772" "V3773" "V3774" "V3775" "V3776" "V3777" "V3778" "V3779" "V3780"
## [3781] "V3781" "V3782" "V3783" "V3784" "V3785" "V3786" "V3787" "V3788" "V3789"
## [3790] "V3790" "V3791" "V3792" "V3793" "V3794" "V3795" "V3796" "V3797" "V3798"
## [3799] "V3799" "V3800" "V3801" "V3802" "V3803" "V3804" "V3805" "V3806" "V3807"
## [3808] "V3808" "V3809" "V3810" "V3811" "V3812" "V3813" "V3814" "V3815" "V3816"
## [3817] "V3817" "V3818" "V3819" "V3820" "V3821" "V3822" "V3823" "V3824" "V3825"
## [3826] "V3826" "V3827" "V3828" "V3829" "V3830" "V3831" "V3832" "V3833" "V3834"
## [3835] "V3835" "V3836" "V3837" "V3838" "V3839" "V3840" "V3841" "V3842" "V3843"
## [3844] "V3844" "V3845" "V3846" "V3847" "V3848" "V3849" "V3850" "V3851" "V3852"
## [3853] "V3853" "V3854" "V3855" "V3856" "V3857" "V3858" "V3859" "V3860" "V3861"
## [3862] "V3862" "V3863" "V3864" "V3865" "V3866" "V3867" "V3868" "V3869" "V3870"
## [3871] "V3871" "V3872" "V3873" "V3874" "V3875" "V3876" "V3877" "V3878" "V3879"
## [3880] "V3880" "V3881" "V3882" "V3883" "V3884" "V3885" "V3886" "V3887" "V3888"
## [3889] "V3889" "V3890" "V3891" "V3892" "V3893" "V3894" "V3895" "V3896" "V3897"
## [3898] "V3898" "V3899" "V3900" "V3901" "V3902" "V3903" "V3904" "V3905" "V3906"
## [3907] "V3907" "V3908" "V3909" "V3910" "V3911" "V3912" "V3913" "V3914" "V3915"
## [3916] "V3916" "V3917" "V3918" "V3919" "V3920" "V3921" "V3922" "V3923" "V3924"
## [3925] "V3925" "V3926" "V3927" "V3928" "V3929" "V3930" "V3931" "V3932" "V3933"
## [3934] "V3934" "V3935" "V3936" "V3937" "V3938" "V3939" "V3940" "V3941" "V3942"
## [3943] "V3943" "V3944" "V3945" "V3946" "V3947" "V3948" "V3949" "V3950" "V3951"
## [3952] "V3952" "V3953" "V3954" "V3955" "V3956" "V3957" "V3958" "V3959" "V3960"
## [3961] "V3961" "V3962" "V3963" "V3964" "V3965" "V3966" "V3967" "V3968" "V3969"
## [3970] "V3970" "V3971" "V3972" "V3973" "V3974" "V3975" "V3976" "V3977" "V3978"
## [3979] "V3979" "V3980" "V3981" "V3982" "V3983" "V3984" "V3985" "V3986" "V3987"
## [3988] "V3988" "V3989" "V3990" "V3991" "V3992" "V3993" "V3994" "V3995" "V3996"
## [3997] "V3997" "V3998" "V3999" "V4000" "V4001" "V4002" "V4003" "V4004" "V4005"
## [4006] "V4006" "V4007" "V4008" "V4009" "V4010" "V4011" "V4012" "V4013" "V4014"
## [4015] "V4015" "V4016" "V4017" "V4018" "V4019" "V4020" "V4021" "V4022" "V4023"
## [4024] "V4024" "V4025" "V4026" "V4027" "V4028" "V4029" "V4030" "V4031" "V4032"
## [4033] "V4033" "V4034" "V4035" "V4036" "V4037" "V4038" "V4039" "V4040" "V4041"
## [4042] "V4042" "V4043" "V4044" "V4045" "V4046" "V4047" "V4048" "V4049" "V4050"
## [4051] "V4051" "V4052" "V4053" "V4054" "V4055" "V4056" "V4057" "V4058" "V4059"
## [4060] "V4060" "V4061" "V4062" "V4063" "V4064" "V4065" "V4066" "V4067" "V4068"
## [4069] "V4069" "V4070" "V4071" "V4072" "V4073" "V4074" "V4075" "V4076" "V4077"
## [4078] "V4078" "V4079" "V4080" "V4081" "V4082" "V4083" "V4084" "V4085" "V4086"
## [4087] "V4087" "V4088" "V4089" "V4090" "V4091" "V4092" "V4093" "V4094" "V4095"
## [4096] "V4096" "V4097" "V4098" "V4099" "V4100" "V4101" "V4102" "V4103" "V4104"
## [4105] "V4105" "V4106" "V4107" "V4108" "V4109" "V4110" "V4111" "V4112" "V4113"
## [4114] "V4114" "V4115" "V4116" "V4117" "V4118" "V4119" "V4120" "V4121" "V4122"
## [4123] "V4123" "V4124" "V4125" "V4126" "V4127" "V4128" "V4129" "V4130" "V4131"
## [4132] "V4132" "V4133" "V4134" "V4135" "V4136" "V4137" "V4138" "V4139" "V4140"
## [4141] "V4141" "V4142" "V4143" "V4144" "V4145" "V4146" "V4147" "V4148" "V4149"
## [4150] "V4150" "V4151" "V4152" "V4153" "V4154" "V4155" "V4156" "V4157" "V4158"
## [4159] "V4159" "V4160" "V4161" "V4162" "V4163" "V4164" "V4165" "V4166" "V4167"
## [4168] "V4168" "V4169" "V4170" "V4171" "V4172" "V4173" "V4174" "V4175" "V4176"
## [4177] "V4177" "V4178" "V4179" "V4180" "V4181" "V4182" "V4183" "V4184" "V4185"
## [4186] "V4186" "V4187" "V4188" "V4189" "V4190" "V4191" "V4192" "V4193" "V4194"
## [4195] "V4195" "V4196" "V4197" "V4198" "V4199" "V4200" "V4201" "V4202" "V4203"
## [4204] "V4204" "V4205" "V4206" "V4207" "V4208" "V4209" "V4210" "V4211" "V4212"
## [4213] "V4213" "V4214" "V4215" "V4216" "V4217" "V4218" "V4219" "V4220" "V4221"
## [4222] "V4222" "V4223" "V4224" "V4225" "V4226" "V4227" "V4228" "V4229" "V4230"
## [4231] "V4231" "V4232" "V4233" "V4234" "V4235" "V4236" "V4237" "V4238" "V4239"
## [4240] "V4240" "V4241" "V4242" "V4243" "V4244" "V4245" "V4246" "V4247" "V4248"
## [4249] "V4249" "V4250" "V4251" "V4252" "V4253" "V4254" "V4255" "V4256" "V4257"
## [4258] "V4258" "V4259" "V4260" "V4261" "V4262" "V4263" "V4264" "V4265" "V4266"
## [4267] "V4267" "V4268" "V4269" "V4270" "V4271" "V4272" "V4273" "V4274" "V4275"
## [4276] "V4276" "V4277" "V4278" "V4279" "V4280" "V4281" "V4282" "V4283" "V4284"
## [4285] "V4285" "V4286" "V4287" "V4288" "V4289" "V4290" "V4291" "V4292" "V4293"
## [4294] "V4294" "V4295" "V4296" "V4297" "V4298" "V4299" "V4300" "V4301" "V4302"
## [4303] "V4303" "V4304" "V4305" "V4306" "V4307" "V4308" "V4309" "V4310" "V4311"
## [4312] "V4312" "V4313" "V4314" "V4315" "V4316" "V4317" "V4318" "V4319" "V4320"
## [4321] "V4321" "V4322" "V4323" "V4324" "V4325" "V4326" "V4327" "V4328" "V4329"
## [4330] "V4330" "V4331" "V4332" "V4333" "V4334" "V4335" "V4336" "V4337" "V4338"
## [4339] "V4339" "V4340" "V4341" "V4342" "V4343" "V4344" "V4345" "V4346" "V4347"
## [4348] "V4348" "V4349" "V4350" "V4351" "V4352" "V4353" "V4354" "V4355" "V4356"
## [4357] "V4357" "V4358" "V4359" "V4360" "V4361" "V4362" "V4363" "V4364" "V4365"
## [4366] "V4366" "V4367" "V4368" "V4369" "V4370" "V4371" "V4372" "V4373" "V4374"
## [4375] "V4375" "V4376" "V4377" "V4378" "V4379" "V4380" "V4381" "V4382" "V4383"
## [4384] "V4384" "V4385" "V4386" "V4387" "V4388" "V4389" "V4390" "V4391" "V4392"
## [4393] "V4393" "V4394" "V4395" "V4396" "V4397" "V4398" "V4399" "V4400" "V4401"
## [4402] "V4402" "V4403" "V4404" "V4405" "V4406" "V4407" "V4408" "V4409" "V4410"
## [4411] "V4411" "V4412" "V4413" "V4414" "V4415" "V4416" "V4417" "V4418" "V4419"
## [4420] "V4420" "V4421" "V4422" "V4423" "V4424" "V4425" "V4426" "V4427" "V4428"
## [4429] "V4429" "V4430" "V4431" "V4432" "V4433" "V4434" "V4435" "V4436" "V4437"
## [4438] "V4438" "V4439" "V4440" "V4441" "V4442" "V4443" "V4444" "V4445" "V4446"
## [4447] "V4447" "V4448" "V4449" "V4450" "V4451" "V4452" "V4453" "V4454" "V4455"
## [4456] "V4456" "V4457" "V4458" "V4459" "V4460" "V4461" "V4462" "V4463" "V4464"
## [4465] "V4465" "V4466" "V4467" "V4468" "V4469" "V4470" "V4471" "V4472" "V4473"
## [4474] "V4474" "V4475" "V4476" "V4477" "V4478" "V4479" "V4480" "V4481" "V4482"
## [4483] "V4483" "V4484" "V4485" "V4486" "V4487" "V4488" "V4489" "V4490" "V4491"
## [4492] "V4492" "V4493" "V4494" "V4495" "V4496" "V4497" "V4498" "V4499" "V4500"
## [4501] "V4501" "V4502" "V4503" "V4504" "V4505" "V4506" "V4507" "V4508" "V4509"
## [4510] "V4510" "V4511" "V4512" "V4513" "V4514" "V4515" "V4516" "V4517" "V4518"
## [4519] "V4519" "V4520" "V4521" "V4522" "V4523" "V4524" "V4525" "V4526" "V4527"
## [4528] "V4528" "V4529" "V4530" "V4531" "V4532" "V4533" "V4534" "V4535" "V4536"
## [4537] "V4537" "V4538" "V4539" "V4540" "V4541" "V4542" "V4543" "V4544" "V4545"
## [4546] "V4546" "V4547" "V4548" "V4549" "V4550" "V4551" "V4552" "V4553" "V4554"
## [4555] "V4555" "V4556" "V4557" "V4558" "V4559" "V4560" "V4561" "V4562" "V4563"
## [4564] "V4564" "V4565" "V4566" "V4567" "V4568" "V4569" "V4570" "V4571" "V4572"
## [4573] "V4573" "V4574" "V4575" "V4576" "V4577" "V4578" "V4579" "V4580" "V4581"
## [4582] "V4582" "V4583" "V4584" "V4585" "V4586" "V4587" "V4588" "V4589" "V4590"
## [4591] "V4591" "V4592" "V4593" "V4594" "V4595" "V4596" "V4597" "V4598" "V4599"
## [4600] "V4600" "V4601" "V4602" "V4603" "V4604" "V4605" "V4606" "V4607" "V4608"
## [4609] "V4609" "V4610" "V4611" "V4612" "V4613" "V4614" "V4615" "V4616" "V4617"
## [4618] "V4618" "V4619" "V4620" "V4621" "V4622" "V4623" "V4624" "V4625" "V4626"
## [4627] "V4627" "V4628" "V4629" "V4630" "V4631" "V4632" "V4633" "V4634" "V4635"
## [4636] "V4636" "V4637" "V4638" "V4639" "V4640" "V4641" "V4642" "V4643" "V4644"
## [4645] "V4645" "V4646" "V4647" "V4648" "V4649" "V4650" "V4651" "V4652" "V4653"
## [4654] "V4654" "V4655" "V4656" "V4657" "V4658" "V4659" "V4660" "V4661" "V4662"
## [4663] "V4663" "V4664" "V4665" "V4666" "V4667" "V4668" "V4669" "V4670" "V4671"
## [4672] "V4672" "V4673" "V4674" "V4675" "V4676" "V4677" "V4678" "V4679" "V4680"
## [4681] "V4681" "V4682" "V4683" "V4684" "V4685" "V4686" "V4687" "V4688" "V4689"
## [4690] "V4690" "V4691" "V4692" "V4693" "V4694" "V4695" "V4696" "V4697" "V4698"
## [4699] "V4699" "V4700" "V4701" "V4702" "V4703" "V4704" "V4705" "V4706" "V4707"
## [4708] "V4708" "V4709" "V4710" "V4711" "V4712" "V4713" "V4714" "V4715" "V4716"
## [4717] "V4717" "V4718" "V4719" "V4720" "V4721" "V4722" "V4723" "V4724" "V4725"
## [4726] "V4726" "V4727" "V4728" "V4729" "V4730" "V4731" "V4732" "V4733" "V4734"
## [4735] "V4735" "V4736" "V4737" "V4738" "V4739" "V4740" "V4741" "V4742" "V4743"
## [4744] "V4744" "V4745" "V4746" "V4747" "V4748" "V4749" "V4750" "V4751" "V4752"
## [4753] "V4753" "V4754" "V4755" "V4756" "V4757" "V4758" "V4759" "V4760" "V4761"
## [4762] "V4762" "V4763" "V4764" "V4765" "V4766" "V4767" "V4768" "V4769" "V4770"
## [4771] "V4771" "V4772" "V4773" "V4774" "V4775" "V4776" "V4777" "V4778" "V4779"
## [4780] "V4780" "V4781" "V4782" "V4783" "V4784" "V4785" "V4786" "V4787" "V4788"
## [4789] "V4789" "V4790" "V4791" "V4792" "V4793" "V4794" "V4795" "V4796" "V4797"
## [4798] "V4798" "V4799" "V4800" "V4801" "V4802" "V4803" "V4804" "V4805" "V4806"
## [4807] "V4807" "V4808" "V4809" "V4810" "V4811" "V4812" "V4813" "V4814" "V4815"
## [4816] "V4816" "V4817" "V4818" "V4819" "V4820" "V4821" "V4822" "V4823" "V4824"
## [4825] "V4825" "V4826" "V4827" "V4828" "V4829" "V4830" "V4831" "V4832" "V4833"
## [4834] "V4834" "V4835" "V4836" "V4837" "V4838" "V4839" "V4840" "V4841" "V4842"
## [4843] "V4843" "V4844" "V4845" "V4846" "V4847" "V4848" "V4849" "V4850" "V4851"
## [4852] "V4852" "V4853" "V4854" "V4855" "V4856" "V4857" "V4858" "V4859" "V4860"
## [4861] "V4861" "V4862" "V4863" "V4864" "V4865" "V4866" "V4867" "V4868" "V4869"
## [4870] "V4870" "V4871" "V4872" "V4873" "V4874" "V4875" "V4876" "V4877" "V4878"
## [4879] "V4879" "V4880" "V4881" "V4882" "V4883" "V4884" "V4885" "V4886" "V4887"
## [4888] "V4888" "V4889" "V4890" "V4891" "V4892" "V4893" "V4894" "V4895" "V4896"
## [4897] "V4897" "V4898" "V4899" "V4900" "V4901" "V4902" "V4903" "V4904" "V4905"
## [4906] "V4906" "V4907" "V4908" "V4909" "V4910" "V4911" "V4912" "V4913" "V4914"
## [4915] "V4915" "V4916" "V4917" "V4918" "V4919" "V4920" "V4921" "V4922" "V4923"
## [4924] "V4924" "V4925" "V4926" "V4927" "V4928" "V4929" "V4930" "V4931" "V4932"
## [4933] "V4933" "V4934" "V4935" "V4936" "V4937" "V4938" "V4939" "V4940" "V4941"
## [4942] "V4942" "V4943" "V4944" "V4945" "V4946" "V4947" "V4948" "V4949" "V4950"
## [4951] "V4951" "V4952" "V4953" "V4954" "V4955" "V4956" "V4957" "V4958" "V4959"
## [4960] "V4960" "V4961" "V4962" "V4963" "V4964" "V4965" "V4966" "V4967" "V4968"
## [4969] "V4969" "V4970" "V4971" "V4972" "V4973" "V4974" "V4975" "V4976" "V4977"
## [4978] "V4978" "V4979" "V4980" "V4981" "V4982" "V4983" "V4984" "V4985" "V4986"
## [4987] "V4987" "V4988" "V4989" "V4990" "V4991" "V4992" "V4993" "V4994" "V4995"
## [4996] "V4996" "V4997" "V4998" "V4999" "V5000" "V5001" "V5002" "V5003" "V5004"
## [5005] "V5005" "V5006" "V5007" "V5008" "V5009" "V5010" "V5011" "V5012" "V5013"
## [5014] "V5014" "V5015" "V5016" "V5017" "V5018" "V5019" "V5020" "V5021" "V5022"
## [5023] "V5023" "V5024" "V5025" "V5026" "V5027" "V5028" "V5029" "V5030" "V5031"
## [5032] "V5032" "V5033" "V5034" "V5035" "V5036" "V5037" "V5038" "V5039" "V5040"
## [5041] "V5041" "V5042" "V5043" "V5044" "V5045" "V5046" "V5047" "V5048" "V5049"
## [5050] "V5050" "V5051" "V5052" "V5053" "V5054" "V5055" "V5056" "V5057" "V5058"
## [5059] "V5059" "V5060" "V5061" "V5062" "V5063" "V5064" "V5065" "V5066" "V5067"
## [5068] "V5068" "V5069" "V5070" "V5071" "V5072" "V5073" "V5074" "V5075" "V5076"
## [5077] "V5077" "V5078" "V5079" "V5080" "V5081" "V5082" "V5083" "V5084" "V5085"
## [5086] "V5086" "V5087" "V5088" "V5089" "V5090" "V5091" "V5092" "V5093" "V5094"
## [5095] "V5095" "V5096" "V5097" "V5098" "V5099" "V5100" "V5101" "V5102" "V5103"
## [5104] "V5104" "V5105" "V5106" "V5107" "V5108" "V5109" "V5110" "V5111" "V5112"
## [5113] "V5113" "V5114" "V5115" "V5116" "V5117" "V5118" "V5119" "V5120" "V5121"
## [5122] "V5122" "V5123" "V5124" "V5125" "V5126" "V5127" "V5128" "V5129" "V5130"
## [5131] "V5131" "V5132" "V5133" "V5134" "V5135" "V5136" "V5137" "V5138" "V5139"
## [5140] "V5140" "V5141" "V5142" "V5143" "V5144" "V5145" "V5146" "V5147" "V5148"
## [5149] "V5149" "V5150" "V5151" "V5152" "V5153" "V5154" "V5155" "V5156" "V5157"
## [5158] "V5158" "V5159" "V5160" "V5161" "V5162" "V5163" "V5164" "V5165" "V5166"
## [5167] "V5167" "V5168" "V5169" "V5170" "V5171" "V5172" "V5173" "V5174" "V5175"
## [5176] "V5176" "V5177" "V5178" "V5179" "V5180" "V5181" "V5182" "V5183" "V5184"
## [5185] "V5185" "V5186" "V5187" "V5188" "V5189" "V5190" "V5191" "V5192" "V5193"
## [5194] "V5194" "V5195" "V5196" "V5197" "V5198" "V5199" "V5200" "V5201" "V5202"
## [5203] "V5203" "V5204" "V5205" "V5206" "V5207" "V5208" "V5209" "V5210" "V5211"
## [5212] "V5212" "V5213" "V5214" "V5215" "V5216" "V5217" "V5218" "V5219" "V5220"
## [5221] "V5221" "V5222" "V5223" "V5224" "V5225" "V5226" "V5227" "V5228" "V5229"
## [5230] "V5230" "V5231" "V5232" "V5233" "V5234" "V5235" "V5236" "V5237" "V5238"
## [5239] "V5239" "V5240" "V5241" "V5242" "V5243" "V5244" "V5245" "V5246" "V5247"
## [5248] "V5248" "V5249" "V5250" "V5251" "V5252" "V5253" "V5254" "V5255" "V5256"
## [5257] "V5257" "V5258" "V5259" "V5260" "V5261" "V5262" "V5263" "V5264" "V5265"
## [5266] "V5266" "V5267" "V5268" "V5269" "V5270" "V5271" "V5272" "V5273" "V5274"
## [5275] "V5275" "V5276" "V5277" "V5278" "V5279" "V5280" "V5281" "V5282" "V5283"
## [5284] "V5284" "V5285" "V5286" "V5287" "V5288" "V5289" "V5290" "V5291" "V5292"
## [5293] "V5293" "V5294" "V5295" "V5296" "V5297" "V5298" "V5299" "V5300" "V5301"
## [5302] "V5302" "V5303" "V5304" "V5305" "V5306" "V5307" "V5308" "V5309" "V5310"
## [5311] "V5311" "V5312" "V5313" "V5314" "V5315" "V5316" "V5317" "V5318" "V5319"
## [5320] "V5320" "V5321" "V5322" "V5323" "V5324" "V5325" "V5326" "V5327" "V5328"
## [5329] "V5329" "V5330" "V5331" "V5332" "V5333" "V5334" "V5335" "V5336" "V5337"
## [5338] "V5338" "V5339" "V5340" "V5341" "V5342" "V5343" "V5344" "V5345" "V5346"
## [5347] "V5347" "V5348" "V5349" "V5350" "V5351" "V5352" "V5353" "V5354" "V5355"
## [5356] "V5356" "V5357" "V5358" "V5359" "V5360" "V5361" "V5362" "V5363" "V5364"
## [5365] "V5365" "V5366" "V5367" "V5368" "V5369" "V5370" "V5371" "V5372" "V5373"
## [5374] "V5374" "V5375" "V5376" "V5377" "V5378" "V5379" "V5380" "V5381" "V5382"
## [5383] "V5383" "V5384" "V5385" "V5386" "V5387" "V5388" "V5389" "V5390" "V5391"
## [5392] "V5392" "V5393" "V5394" "V5395" "V5396" "V5397" "V5398" "V5399" "V5400"
## [5401] "V5401" "V5402" "V5403" "V5404" "V5405" "V5406" "V5407" "V5408" "V5409"
## [5410] "V5410" "V5411" "V5412" "V5413" "V5414" "V5415" "V5416" "V5417" "V5418"
## [5419] "V5419" "V5420" "V5421" "V5422" "V5423" "V5424" "V5425" "V5426" "V5427"
## [5428] "V5428" "V5429" "V5430" "V5431" "V5432" "V5433" "V5434" "V5435" "V5436"
## [5437] "V5437" "V5438" "V5439" "V5440" "V5441" "V5442" "V5443" "V5444" "V5445"
## [5446] "V5446" "V5447" "V5448" "V5449" "V5450" "V5451" "V5452" "V5453" "V5454"
## [5455] "V5455" "V5456" "V5457" "V5458" "V5459" "V5460" "V5461" "V5462" "V5463"
## [5464] "V5464" "V5465" "V5466" "V5467" "V5468" "V5469" "V5470" "V5471" "V5472"
## [5473] "V5473" "V5474" "V5475" "V5476" "V5477" "V5478" "V5479" "V5480" "V5481"
## [5482] "V5482" "V5483" "V5484" "V5485" "V5486" "V5487" "V5488" "V5489" "V5490"
## [5491] "V5491" "V5492" "V5493" "V5494" "V5495" "V5496" "V5497" "V5498" "V5499"
## [5500] "V5500" "V5501" "V5502" "V5503" "V5504" "V5505" "V5506" "V5507" "V5508"
## [5509] "V5509" "V5510" "V5511" "V5512" "V5513" "V5514" "V5515" "V5516" "V5517"
## [5518] "V5518" "V5519" "V5520" "V5521" "V5522" "V5523" "V5524" "V5525" "V5526"
## [5527] "V5527" "V5528" "V5529" "V5530" "V5531" "V5532" "V5533" "V5534" "V5535"
## [5536] "V5536" "V5537" "V5538" "V5539" "V5540" "V5541" "V5542" "V5543" "V5544"
## [5545] "V5545" "V5546" "V5547" "V5548" "V5549" "V5550" "V5551" "V5552" "V5553"
## [5554] "V5554" "V5555" "V5556" "V5557" "V5558" "V5559" "V5560" "V5561" "V5562"
## [5563] "V5563" "V5564" "V5565" "V5566" "V5567" "V5568" "V5569" "V5570" "V5571"
## [5572] "V5572" "V5573" "V5574" "V5575" "V5576" "V5577" "V5578" "V5579" "V5580"
## [5581] "V5581" "V5582" "V5583" "V5584" "V5585" "V5586" "V5587" "V5588" "V5589"
## [5590] "V5590" "V5591" "V5592" "V5593" "V5594" "V5595" "V5596" "V5597" "V5598"
## [5599] "V5599" "V5600" "V5601" "V5602" "V5603" "V5604" "V5605" "V5606" "V5607"
## [5608] "V5608" "V5609" "V5610" "V5611" "V5612" "V5613" "V5614" "V5615" "V5616"
## [5617] "V5617" "V5618" "V5619" "V5620" "V5621" "V5622" "V5623" "V5624" "V5625"
## [5626] "V5626" "V5627" "V5628" "V5629" "V5630" "V5631" "V5632" "V5633" "V5634"
## [5635] "V5635" "V5636" "V5637" "V5638" "V5639" "V5640" "V5641" "V5642" "V5643"
## [5644] "V5644" "V5645" "V5646" "V5647" "V5648" "V5649" "V5650" "V5651" "V5652"
## [5653] "V5653" "V5654" "V5655" "V5656" "V5657" "V5658" "V5659" "V5660" "V5661"
## [5662] "V5662" "V5663" "V5664" "V5665" "V5666" "V5667" "V5668" "V5669" "V5670"
## [5671] "V5671" "V5672" "V5673" "V5674" "V5675" "V5676" "V5677" "V5678" "V5679"
## [5680] "V5680" "V5681" "V5682" "V5683" "V5684" "V5685" "V5686" "V5687" "V5688"
## [5689] "V5689" "V5690" "V5691" "V5692" "V5693" "V5694" "V5695" "V5696" "V5697"
## [5698] "V5698" "V5699" "V5700" "V5701" "V5702" "V5703" "V5704" "V5705" "V5706"
## [5707] "V5707" "V5708" "V5709" "V5710" "V5711" "V5712" "V5713" "V5714" "V5715"
## [5716] "V5716" "V5717" "V5718" "V5719" "V5720" "V5721" "V5722" "V5723" "V5724"
## [5725] "V5725" "V5726" "V5727" "V5728" "V5729" "V5730" "V5731" "V5732" "V5733"
## [5734] "V5734" "V5735" "V5736" "V5737" "V5738" "V5739" "V5740" "V5741" "V5742"
## [5743] "V5743" "V5744" "V5745" "V5746" "V5747" "V5748" "V5749" "V5750" "V5751"
## [5752] "V5752" "V5753" "V5754" "V5755" "V5756" "V5757" "V5758" "V5759" "V5760"
## [5761] "V5761" "V5762" "V5763" "V5764" "V5765" "V5766" "V5767" "V5768" "V5769"
## [5770] "V5770" "V5771" "V5772" "V5773" "V5774" "V5775" "V5776" "V5777" "V5778"
## [5779] "V5779" "V5780" "V5781" "V5782" "V5783" "V5784" "V5785" "V5786" "V5787"
## [5788] "V5788" "V5789" "V5790" "V5791" "V5792" "V5793" "V5794" "V5795" "V5796"
## [5797] "V5797" "V5798" "V5799" "V5800" "V5801" "V5802" "V5803" "V5804" "V5805"
## [5806] "V5806" "V5807" "V5808" "V5809" "V5810" "V5811" "V5812" "V5813" "V5814"
## [5815] "V5815" "V5816" "V5817" "V5818" "V5819" "V5820" "V5821" "V5822" "V5823"
## [5824] "V5824" "V5825" "V5826" "V5827" "V5828" "V5829" "V5830" "V5831" "V5832"
## [5833] "V5833" "V5834" "V5835" "V5836" "V5837" "V5838" "V5839" "V5840" "V5841"
## [5842] "V5842" "V5843" "V5844" "V5845" "V5846" "V5847" "V5848" "V5849" "V5850"
## [5851] "V5851" "V5852" "V5853" "V5854" "V5855" "V5856" "V5857" "V5858" "V5859"
## [5860] "V5860" "V5861" "V5862" "V5863" "V5864" "V5865" "V5866" "V5867" "V5868"
## [5869] "V5869" "V5870" "V5871" "V5872" "V5873" "V5874" "V5875" "V5876" "V5877"
## [5878] "V5878" "V5879" "V5880" "V5881" "V5882" "V5883" "V5884" "V5885" "V5886"
## [5887] "V5887" "V5888" "V5889" "V5890" "V5891" "V5892" "V5893" "V5894" "V5895"
## [5896] "V5896" "V5897" "V5898" "V5899" "V5900" "V5901" "V5902" "V5903" "V5904"
## [5905] "V5905" "V5906" "V5907" "V5908" "V5909" "V5910" "V5911" "V5912" "V5913"
## [5914] "V5914" "V5915" "V5916" "V5917" "V5918" "V5919" "V5920" "V5921" "V5922"
## [5923] "V5923" "V5924" "V5925" "V5926" "V5927" "V5928" "V5929" "V5930" "V5931"
## [5932] "V5932" "V5933" "V5934" "V5935" "V5936" "V5937" "V5938" "V5939" "V5940"
## [5941] "V5941" "V5942" "V5943" "V5944" "V5945" "V5946" "V5947" "V5948" "V5949"
## [5950] "V5950" "V5951" "V5952" "V5953" "V5954" "V5955" "V5956" "V5957" "V5958"
## [5959] "V5959" "V5960" "V5961" "V5962" "V5963" "V5964" "V5965" "V5966" "V5967"
## [5968] "V5968" "V5969" "V5970" "V5971" "V5972" "V5973" "V5974" "V5975" "V5976"
## [5977] "V5977" "V5978" "V5979" "V5980" "V5981" "V5982" "V5983" "V5984" "V5985"
## [5986] "V5986" "V5987" "V5988" "V5989" "V5990" "V5991" "V5992" "V5993" "V5994"
## [5995] "V5995" "V5996" "V5997" "V5998" "V5999" "V6000" "V6001" "V6002" "V6003"
## [6004] "V6004" "V6005" "V6006" "V6007" "V6008" "V6009" "V6010" "V6011" "V6012"
## [6013] "V6013" "V6014" "V6015" "V6016" "V6017" "V6018" "V6019" "V6020" "V6021"
## [6022] "V6022" "V6023" "V6024" "V6025" "V6026" "V6027" "V6028" "V6029" "V6030"
## [6031] "V6031" "V6032" "V6033" "V6034" "V6035" "V6036" "V6037" "V6038" "V6039"
## [6040] "V6040" "V6041" "V6042" "V6043" "V6044" "V6045" "V6046" "V6047" "V6048"
## [6049] "V6049" "V6050" "V6051" "V6052" "V6053" "V6054" "V6055" "V6056" "V6057"
## [6058] "V6058" "V6059" "V6060" "V6061" "V6062" "V6063" "V6064" "V6065" "V6066"
## [6067] "V6067" "V6068" "V6069" "V6070" "V6071" "V6072" "V6073" "V6074" "V6075"
## [6076] "V6076" "V6077" "V6078" "V6079" "V6080" "V6081" "V6082" "V6083" "V6084"
## [6085] "V6085" "V6086" "V6087" "V6088" "V6089" "V6090" "V6091" "V6092" "V6093"
## [6094] "V6094" "V6095" "V6096" "V6097" "V6098" "V6099" "V6100" "V6101" "V6102"
## [6103] "V6103" "V6104" "V6105" "V6106" "V6107" "V6108" "V6109" "V6110" "V6111"
## [6112] "V6112" "V6113" "V6114" "V6115" "V6116" "V6117" "V6118" "V6119" "V6120"
## [6121] "V6121" "V6122" "V6123" "V6124" "V6125" "V6126" "V6127" "V6128" "V6129"
## [6130] "V6130" "V6131" "V6132" "V6133" "V6134" "V6135" "V6136" "V6137" "V6138"
## [6139] "V6139" "V6140" "V6141" "V6142" "V6143" "V6144" "V6145" "V6146" "V6147"
## [6148] "V6148" "V6149" "V6150" "V6151" "V6152" "V6153" "V6154" "V6155" "V6156"
## [6157] "V6157" "V6158" "V6159" "V6160" "V6161" "V6162" "V6163" "V6164" "V6165"
## [6166] "V6166" "V6167" "V6168" "V6169" "V6170" "V6171" "V6172" "V6173" "V6174"
## [6175] "V6175" "V6176" "V6177" "V6178" "V6179" "V6180" "V6181" "V6182" "V6183"
## [6184] "V6184" "V6185" "V6186" "V6187" "V6188" "V6189" "V6190" "V6191" "V6192"
## [6193] "V6193" "V6194" "V6195" "V6196" "V6197" "V6198" "V6199" "V6200" "V6201"
## [6202] "V6202" "V6203" "V6204" "V6205" "V6206" "V6207" "V6208" "V6209" "V6210"
## [6211] "V6211" "V6212" "V6213" "V6214" "V6215" "V6216" "V6217" "V6218" "V6219"
## [6220] "V6220" "V6221" "V6222" "V6223" "V6224" "V6225" "V6226" "V6227" "V6228"
## [6229] "V6229" "V6230" "V6231" "V6232" "V6233" "V6234" "V6235" "V6236" "V6237"
## [6238] "V6238" "V6239" "V6240" "V6241" "V6242" "V6243" "V6244" "V6245" "V6246"
## [6247] "V6247" "V6248" "V6249" "V6250" "V6251" "V6252" "V6253" "V6254" "V6255"
## [6256] "V6256" "V6257" "V6258" "V6259" "V6260" "V6261" "V6262" "V6263" "V6264"
## [6265] "V6265" "V6266" "V6267" "V6268" "V6269" "V6270" "V6271" "V6272" "V6273"
## [6274] "V6274" "V6275" "V6276" "V6277" "V6278" "V6279" "V6280" "V6281" "V6282"
## [6283] "V6283" "V6284" "V6285" "V6286" "V6287" "V6288" "V6289" "V6290" "V6291"
## [6292] "V6292" "V6293" "V6294" "V6295" "V6296" "V6297" "V6298" "V6299" "V6300"
## [6301] "V6301" "V6302" "V6303" "V6304" "V6305" "V6306" "V6307" "V6308" "V6309"
## [6310] "V6310" "V6311" "V6312" "V6313" "V6314" "V6315" "V6316" "V6317" "V6318"
## [6319] "V6319" "V6320" "V6321" "V6322" "V6323" "V6324" "V6325" "V6326" "V6327"
## [6328] "V6328" "V6329" "V6330" "V6331" "V6332" "V6333" "V6334" "V6335" "V6336"
## [6337] "V6337" "V6338" "V6339" "V6340" "V6341" "V6342" "V6343" "V6344" "V6345"
## [6346] "V6346" "V6347" "V6348" "V6349" "V6350" "V6351" "V6352" "V6353" "V6354"
## [6355] "V6355" "V6356" "V6357" "V6358" "V6359" "V6360" "V6361" "V6362" "V6363"
## [6364] "V6364" "V6365" "V6366" "V6367" "V6368" "V6369" "V6370" "V6371" "V6372"
## [6373] "V6373" "V6374" "V6375" "V6376" "V6377" "V6378" "V6379" "V6380" "V6381"
## [6382] "V6382" "V6383" "V6384" "V6385" "V6386" "V6387" "V6388" "V6389" "V6390"
## [6391] "V6391" "V6392" "V6393" "V6394" "V6395" "V6396" "V6397" "V6398" "V6399"
## [6400] "V6400" "V6401" "V6402" "V6403" "V6404" "V6405" "V6406" "V6407" "V6408"
## [6409] "V6409" "V6410" "V6411" "V6412" "V6413" "V6414" "V6415" "V6416" "V6417"
## [6418] "V6418" "V6419" "V6420" "V6421" "V6422" "V6423" "V6424" "V6425" "V6426"
## [6427] "V6427" "V6428" "V6429" "V6430" "V6431" "V6432" "V6433" "V6434" "V6435"
## [6436] "V6436" "V6437" "V6438" "V6439" "V6440" "V6441" "V6442" "V6443" "V6444"
## [6445] "V6445" "V6446" "V6447" "V6448" "V6449" "V6450" "V6451" "V6452" "V6453"
## [6454] "V6454" "V6455" "V6456" "V6457" "V6458" "V6459" "V6460" "V6461" "V6462"
## [6463] "V6463" "V6464" "V6465" "V6466" "V6467" "V6468" "V6469" "V6470" "V6471"
## [6472] "V6472" "V6473" "V6474" "V6475" "V6476" "V6477" "V6478" "V6479" "V6480"
## [6481] "V6481" "V6482" "V6483" "V6484" "V6485" "V6486" "V6487" "V6488" "V6489"
## [6490] "V6490" "V6491" "V6492" "V6493" "V6494" "V6495" "V6496" "V6497" "V6498"
## [6499] "V6499" "V6500" "V6501" "V6502" "V6503" "V6504" "V6505" "V6506" "V6507"
## [6508] "V6508" "V6509" "V6510" "V6511" "V6512" "V6513" "V6514" "V6515" "V6516"
## [6517] "V6517" "V6518" "V6519" "V6520" "V6521" "V6522" "V6523" "V6524" "V6525"
## [6526] "V6526" "V6527" "V6528" "V6529" "V6530" "V6531" "V6532" "V6533" "V6534"
## [6535] "V6535" "V6536" "V6537" "V6538" "V6539" "V6540" "V6541" "V6542" "V6543"
## [6544] "V6544" "V6545" "V6546" "V6547" "V6548" "V6549" "V6550" "V6551" "V6552"
## [6553] "V6553" "V6554" "V6555" "V6556" "V6557" "V6558" "V6559" "V6560" "V6561"
## [6562] "V6562" "V6563" "V6564" "V6565" "V6566" "V6567" "V6568" "V6569" "V6570"
## [6571] "V6571" "V6572" "V6573" "V6574" "V6575" "V6576" "V6577" "V6578" "V6579"
## [6580] "V6580" "V6581" "V6582" "V6583" "V6584" "V6585" "V6586" "V6587" "V6588"
## [6589] "V6589" "V6590" "V6591" "V6592" "V6593" "V6594" "V6595" "V6596" "V6597"
## [6598] "V6598" "V6599" "V6600" "V6601" "V6602" "V6603" "V6604" "V6605" "V6606"
## [6607] "V6607" "V6608" "V6609" "V6610" "V6611" "V6612" "V6613" "V6614" "V6615"
## [6616] "V6616" "V6617" "V6618" "V6619" "V6620" "V6621" "V6622" "V6623" "V6624"
## [6625] "V6625" "V6626" "V6627" "V6628" "V6629" "V6630" "V6631" "V6632" "V6633"
## [6634] "V6634" "V6635" "V6636" "V6637" "V6638" "V6639" "V6640" "V6641" "V6642"
## [6643] "V6643" "V6644" "V6645" "V6646" "V6647" "V6648" "V6649" "V6650" "V6651"
## [6652] "V6652" "V6653" "V6654" "V6655" "V6656" "V6657" "V6658" "V6659" "V6660"
## [6661] "V6661" "V6662" "V6663" "V6664" "V6665" "V6666" "V6667" "V6668" "V6669"
## [6670] "V6670" "V6671" "V6672" "V6673" "V6674" "V6675" "V6676" "V6677" "V6678"
## [6679] "V6679" "V6680" "V6681" "V6682" "V6683" "V6684" "V6685" "V6686" "V6687"
## [6688] "V6688" "V6689" "V6690" "V6691" "V6692" "V6693" "V6694" "V6695" "V6696"
## [6697] "V6697" "V6698" "V6699" "V6700" "V6701" "V6702" "V6703" "V6704" "V6705"
## [6706] "V6706" "V6707" "V6708" "V6709" "V6710" "V6711" "V6712" "V6713" "V6714"
## [6715] "V6715" "V6716" "V6717" "V6718" "V6719" "V6720" "V6721" "V6722" "V6723"
## [6724] "V6724" "V6725" "V6726" "V6727" "V6728" "V6729" "V6730" "V6731" "V6732"
## [6733] "V6733" "V6734" "V6735" "V6736" "V6737" "V6738" "V6739" "V6740" "V6741"
## [6742] "V6742" "V6743" "V6744" "V6745" "V6746" "V6747" "V6748" "V6749" "V6750"
## [6751] "V6751" "V6752" "V6753" "V6754" "V6755" "V6756" "V6757" "V6758" "V6759"
## [6760] "V6760" "V6761" "V6762" "V6763" "V6764" "V6765" "V6766" "V6767" "V6768"
## [6769] "V6769" "V6770" "V6771" "V6772" "V6773" "V6774" "V6775" "V6776" "V6777"
## [6778] "V6778" "V6779" "V6780" "V6781" "V6782" "V6783" "V6784" "V6785" "V6786"
## [6787] "V6787" "V6788" "V6789" "V6790" "V6791" "V6792" "V6793" "V6794" "V6795"
## [6796] "V6796" "V6797" "V6798" "V6799" "V6800" "V6801" "V6802" "V6803" "V6804"
## [6805] "V6805" "V6806" "V6807" "V6808" "V6809" "V6810" "V6811" "V6812" "V6813"
## [6814] "V6814" "V6815" "V6816" "V6817" "V6818" "V6819" "V6820" "V6821" "V6822"
## [6823] "V6823" "V6824" "V6825" "V6826" "V6827" "V6828" "V6829" "V6830" "V6831"
## [6832] "V6832" "V6833" "V6834" "V6835" "V6836" "V6837" "V6838" "V6839" "V6840"
## [6841] "V6841" "V6842" "V6843" "V6844" "V6845" "V6846" "V6847" "V6848" "V6849"
## [6850] "V6850" "V6851" "V6852" "V6853" "V6854" "V6855" "V6856" "V6857" "V6858"
## [6859] "V6859" "V6860" "V6861" "V6862" "V6863" "V6864" "V6865" "V6866" "V6867"
## [6868] "V6868" "V6869" "V6870" "V6871" "V6872" "V6873" "V6874" "V6875" "V6876"
## [6877] "V6877" "V6878" "V6879" "V6880" "V6881" "V6882" "V6883" "V6884" "V6885"
## [6886] "V6886" "V6887" "V6888" "V6889" "V6890" "V6891" "V6892" "V6893" "V6894"
## [6895] "V6895" "V6896" "V6897" "V6898" "V6899" "V6900" "V6901" "V6902" "V6903"
## [6904] "V6904" "V6905" "V6906" "V6907" "V6908" "V6909" "V6910" "V6911" "V6912"
## [6913] "V6913" "V6914" "V6915" "V6916" "V6917" "V6918" "V6919" "V6920" "V6921"
## [6922] "V6922" "V6923" "V6924" "V6925" "V6926" "V6927" "V6928" "V6929" "V6930"
## [6931] "V6931" "V6932" "V6933" "V6934" "V6935" "V6936" "V6937" "V6938" "V6939"
## [6940] "V6940" "V6941" "V6942" "V6943" "V6944" "V6945" "V6946" "V6947" "V6948"
## [6949] "V6949" "V6950" "V6951" "V6952" "V6953" "V6954" "V6955" "V6956" "V6957"
## [6958] "V6958" "V6959" "V6960" "V6961" "V6962" "V6963" "V6964" "V6965" "V6966"
## [6967] "V6967" "V6968" "V6969" "V6970" "V6971" "V6972" "V6973" "V6974" "V6975"
## [6976] "V6976" "V6977" "V6978" "V6979" "V6980" "V6981" "V6982" "V6983" "V6984"
## [6985] "V6985" "V6986" "V6987" "V6988" "V6989" "V6990" "V6991" "V6992" "V6993"
## [6994] "V6994" "V6995" "V6996" "V6997" "V6998" "V6999" "V7000" "V7001" "V7002"
## [7003] "V7003" "V7004" "V7005" "V7006" "V7007" "V7008" "V7009" "V7010" "V7011"
## [7012] "V7012" "V7013" "V7014" "V7015" "V7016" "V7017" "V7018" "V7019" "V7020"
## [7021] "V7021" "V7022" "V7023" "V7024" "V7025" "V7026" "V7027" "V7028" "V7029"
## [7030] "V7030" "V7031" "V7032" "V7033" "V7034" "V7035" "V7036" "V7037" "V7038"
## [7039] "V7039" "V7040" "V7041" "V7042" "V7043" "V7044" "V7045" "V7046" "V7047"
## [7048] "V7048" "V7049" "V7050" "V7051" "V7052" "V7053" "V7054" "V7055" "V7056"
## [7057] "V7057" "V7058" "V7059" "V7060" "V7061" "V7062" "V7063" "V7064" "V7065"
## [7066] "V7066" "V7067" "V7068" "V7069" "V7070" "V7071" "V7072" "V7073" "V7074"
## [7075] "V7075" "V7076" "V7077" "V7078" "V7079" "V7080" "V7081" "V7082" "V7083"
## [7084] "V7084" "V7085" "V7086" "V7087" "V7088" "V7089" "V7090" "V7091" "V7092"
## [7093] "V7093" "V7094" "V7095" "V7096" "V7097" "V7098" "V7099" "V7100" "V7101"
## [7102] "V7102" "V7103" "V7104" "V7105" "V7106" "V7107" "V7108" "V7109" "V7110"
## [7111] "V7111" "V7112" "V7113" "V7114" "V7115" "V7116" "V7117" "V7118" "V7119"
## [7120] "V7120" "V7121" "V7122" "V7123" "V7124" "V7125" "V7126" "V7127" "V7128"
## [7129] "V7129" "V7130" "V7131" "V7132" "V7133" "V7134" "V7135" "V7136" "V7137"
## [7138] "V7138" "V7139" "V7140" "V7141" "V7142" "V7143" "V7144" "V7145" "V7146"
## [7147] "V7147" "V7148" "V7149" "V7150" "V7151" "V7152" "V7153" "V7154" "V7155"
## [7156] "V7156" "V7157" "V7158" "V7159" "V7160" "V7161" "V7162" "V7163" "V7164"
## [7165] "V7165" "V7166" "V7167" "V7168" "V7169" "V7170" "V7171" "V7172" "V7173"
## [7174] "V7174" "V7175" "V7176" "V7177" "V7178" "V7179" "V7180" "V7181" "V7182"
## [7183] "V7183" "V7184" "V7185" "V7186" "V7187" "V7188" "V7189" "V7190" "V7191"
## [7192] "V7192" "V7193" "V7194" "V7195" "V7196" "V7197" "V7198" "V7199" "V7200"
## [7201] "V7201" "V7202" "V7203" "V7204" "V7205" "V7206" "V7207" "V7208" "V7209"
## [7210] "V7210" "V7211" "V7212" "V7213" "V7214" "V7215" "V7216" "V7217" "V7218"
## [7219] "V7219" "V7220" "V7221" "V7222" "V7223" "V7224" "V7225" "V7226" "V7227"
## [7228] "V7228" "V7229" "V7230" "V7231" "V7232" "V7233" "V7234" "V7235" "V7236"
## [7237] "V7237" "V7238" "V7239" "V7240" "V7241" "V7242" "V7243" "V7244" "V7245"
## [7246] "V7246" "V7247" "V7248" "V7249" "V7250" "V7251" "V7252" "V7253" "V7254"
## [7255] "V7255" "V7256" "V7257" "V7258" "V7259" "V7260" "V7261" "V7262" "V7263"
## [7264] "V7264" "V7265" "V7266" "V7267" "V7268" "V7269" "V7270" "V7271" "V7272"
## [7273] "V7273" "V7274" "V7275" "V7276" "V7277" "V7278" "V7279" "V7280" "V7281"
## [7282] "V7282" "V7283" "V7284" "V7285" "V7286" "V7287" "V7288" "V7289" "V7290"
## [7291] "V7291" "V7292" "V7293" "V7294" "V7295" "V7296" "V7297" "V7298" "V7299"
## [7300] "V7300" "V7301" "V7302" "V7303" "V7304" "V7305" "V7306" "V7307" "V7308"
## [7309] "V7309" "V7310" "V7311" "V7312" "V7313" "V7314" "V7315" "V7316" "V7317"
## [7318] "V7318" "V7319" "V7320" "V7321" "V7322" "V7323" "V7324" "V7325" "V7326"
## [7327] "V7327" "V7328" "V7329" "V7330" "V7331" "V7332" "V7333" "V7334" "V7335"
## [7336] "V7336" "V7337" "V7338" "V7339" "V7340" "V7341" "V7342" "V7343" "V7344"
## [7345] "V7345" "V7346" "V7347" "V7348" "V7349" "V7350" "V7351" "V7352" "V7353"
## [7354] "V7354" "V7355" "V7356" "V7357" "V7358" "V7359" "V7360" "V7361" "V7362"
## [7363] "V7363" "V7364" "V7365" "V7366" "V7367" "V7368" "V7369" "V7370" "V7371"
## [7372] "V7372" "V7373" "V7374" "V7375" "V7376" "V7377" "V7378" "V7379" "V7380"
## [7381] "V7381" "V7382" "V7383" "V7384" "V7385" "V7386" "V7387" "V7388" "V7389"
## [7390] "V7390" "V7391" "V7392" "V7393" "V7394" "V7395" "V7396" "V7397" "V7398"
## [7399] "V7399" "V7400" "V7401" "V7402" "V7403" "V7404" "V7405" "V7406" "V7407"
## [7408] "V7408" "V7409" "V7410" "V7411" "V7412" "V7413" "V7414" "V7415" "V7416"
## [7417] "V7417" "V7418" "V7419" "V7420" "V7421" "V7422" "V7423" "V7424" "V7425"
## [7426] "V7426" "V7427" "V7428" "V7429" "V7430" "V7431" "V7432" "V7433" "V7434"
## [7435] "V7435" "V7436" "V7437" "V7438" "V7439" "V7440" "V7441" "V7442" "V7443"
## [7444] "V7444" "V7445" "V7446" "V7447" "V7448" "V7449" "V7450" "V7451" "V7452"
## [7453] "V7453" "V7454" "V7455" "V7456" "V7457" "V7458" "V7459" "V7460" "V7461"
## [7462] "V7462" "V7463" "V7464" "V7465" "V7466" "V7467" "V7468" "V7469" "V7470"
## [7471] "V7471" "V7472" "V7473" "V7474" "V7475" "V7476" "V7477" "V7478" "V7479"
## [7480] "V7480" "V7481" "V7482" "V7483" "V7484" "V7485" "V7486" "V7487" "V7488"
## [7489] "V7489" "V7490" "V7491" "V7492" "V7493" "V7494" "V7495" "V7496" "V7497"
## [7498] "V7498" "V7499" "V7500" "V7501" "V7502" "V7503" "V7504" "V7505" "V7506"
## [7507] "V7507" "V7508" "V7509" "V7510" "V7511" "V7512" "V7513" "V7514" "V7515"
## [7516] "V7516" "V7517" "V7518" "V7519" "V7520" "V7521" "V7522" "V7523" "V7524"
## [7525] "V7525" "V7526" "V7527" "V7528" "V7529" "V7530" "V7531" "V7532" "V7533"
## [7534] "V7534" "V7535" "V7536" "V7537" "V7538" "V7539" "V7540" "V7541" "V7542"
## [7543] "V7543" "V7544" "V7545" "V7546" "V7547" "V7548" "V7549" "V7550" "V7551"
## [7552] "V7552" "V7553" "V7554" "V7555" "V7556" "V7557" "V7558" "V7559" "V7560"
## [7561] "V7561" "V7562" "V7563" "V7564" "V7565" "V7566" "V7567" "V7568" "V7569"
## [7570] "V7570" "V7571" "V7572" "V7573" "V7574" "V7575" "V7576" "V7577" "V7578"
## [7579] "V7579" "V7580" "V7581" "V7582" "V7583" "V7584" "V7585" "V7586" "V7587"
## [7588] "V7588" "V7589" "V7590" "V7591" "V7592" "V7593" "V7594" "V7595" "V7596"
## [7597] "V7597" "V7598" "V7599" "V7600" "V7601" "V7602" "V7603" "V7604" "V7605"
## [7606] "V7606" "V7607" "V7608" "V7609" "V7610" "V7611" "V7612" "V7613" "V7614"
## [7615] "V7615" "V7616" "V7617" "V7618" "V7619" "V7620" "V7621" "V7622" "V7623"
## [7624] "V7624" "V7625" "V7626" "V7627" "V7628" "V7629" "V7630" "V7631" "V7632"
## [7633] "V7633" "V7634" "V7635" "V7636" "V7637" "V7638" "V7639" "V7640" "V7641"
## [7642] "V7642" "V7643" "V7644" "V7645" "V7646" "V7647" "V7648" "V7649" "V7650"
## [7651] "V7651" "V7652" "V7653" "V7654" "V7655" "V7656" "V7657" "V7658" "V7659"
## [7660] "V7660" "V7661" "V7662" "V7663" "V7664" "V7665" "V7666" "V7667" "V7668"
## [7669] "V7669" "V7670" "V7671" "V7672" "V7673" "V7674" "V7675" "V7676" "V7677"
## [7678] "V7678" "V7679" "V7680" "V7681" "V7682" "V7683" "V7684" "V7685" "V7686"
## [7687] "V7687" "V7688" "V7689" "V7690" "V7691" "V7692" "V7693" "V7694" "V7695"
## [7696] "V7696" "V7697" "V7698" "V7699" "V7700" "V7701" "V7702" "V7703" "V7704"
## [7705] "V7705" "V7706" "V7707" "V7708" "V7709" "V7710" "V7711" "V7712" "V7713"
## [7714] "V7714" "V7715" "V7716" "V7717" "V7718" "V7719" "V7720" "V7721" "V7722"
## [7723] "V7723" "V7724" "V7725" "V7726" "V7727" "V7728" "V7729" "V7730" "V7731"
## [7732] "V7732" "V7733" "V7734" "V7735" "V7736" "V7737" "V7738" "V7739" "V7740"
## [7741] "V7741" "V7742" "V7743" "V7744" "V7745" "V7746" "V7747" "V7748" "V7749"
## [7750] "V7750" "V7751" "V7752" "V7753" "V7754" "V7755" "V7756" "V7757" "V7758"
## [7759] "V7759" "V7760" "V7761" "V7762" "V7763" "V7764" "V7765" "V7766" "V7767"
## [7768] "V7768" "V7769" "V7770" "V7771" "V7772" "V7773" "V7774" "V7775" "V7776"
## [7777] "V7777" "V7778" "V7779" "V7780" "V7781" "V7782" "V7783" "V7784" "V7785"
## [7786] "V7786" "V7787" "V7788" "V7789" "V7790" "V7791" "V7792" "V7793" "V7794"
## [7795] "V7795" "V7796" "V7797" "V7798" "V7799" "V7800" "V7801" "V7802" "V7803"
## [7804] "V7804" "V7805" "V7806" "V7807" "V7808" "V7809" "V7810" "V7811" "V7812"
## [7813] "V7813" "V7814" "V7815" "V7816" "V7817" "V7818" "V7819" "V7820" "V7821"
## [7822] "V7822" "V7823" "V7824" "V7825" "V7826" "V7827" "V7828" "V7829" "V7830"
## [7831] "V7831" "V7832" "V7833" "V7834" "V7835" "V7836" "V7837" "V7838" "V7839"
## [7840] "V7840" "V7841" "V7842" "V7843" "V7844" "V7845" "V7846" "V7847" "V7848"
## [7849] "V7849" "V7850" "V7851" "V7852" "V7853" "V7854" "V7855" "V7856" "V7857"
## [7858] "V7858" "V7859" "V7860" "V7861" "V7862" "V7863" "V7864" "V7865" "V7866"
## [7867] "V7867" "V7868" "V7869" "V7870" "V7871" "V7872" "V7873" "V7874" "V7875"
## [7876] "V7876" "V7877" "V7878" "V7879" "V7880" "V7881" "V7882" "V7883" "V7884"
## [7885] "V7885" "V7886" "V7887" "V7888" "V7889" "V7890" "V7891" "V7892" "V7893"
## [7894] "V7894" "V7895" "V7896" "V7897" "V7898" "V7899" "V7900" "V7901" "V7902"
## [7903] "V7903" "V7904" "V7905" "V7906" "V7907" "V7908" "V7909" "V7910" "V7911"
## [7912] "V7912" "V7913" "V7914" "V7915" "V7916" "V7917" "V7918" "V7919" "V7920"
## [7921] "V7921" "V7922" "V7923" "V7924" "V7925" "V7926" "V7927" "V7928" "V7929"
## [7930] "V7930" "V7931" "V7932" "V7933" "V7934" "V7935" "V7936" "V7937" "V7938"
## [7939] "V7939" "V7940" "V7941" "V7942" "V7943" "V7944" "V7945" "V7946" "V7947"
## [7948] "V7948" "V7949" "V7950" "V7951" "V7952" "V7953" "V7954" "V7955" "V7956"
## [7957] "V7957" "V7958" "V7959" "V7960" "V7961" "V7962" "V7963" "V7964" "V7965"
## [7966] "V7966" "V7967" "V7968" "V7969" "V7970" "V7971" "V7972" "V7973" "V7974"
## [7975] "V7975" "V7976" "V7977" "V7978" "V7979" "V7980" "V7981" "V7982" "V7983"
## [7984] "V7984" "V7985" "V7986" "V7987" "V7988" "V7989" "V7990" "V7991" "V7992"
## [7993] "V7993" "V7994" "V7995" "V7996" "V7997" "V7998" "V7999" "V8000" "V8001"
## [8002] "V8002" "V8003" "V8004" "V8005" "V8006" "V8007" "V8008" "V8009" "V8010"
## [8011] "V8011" "V8012" "V8013" "V8014" "V8015" "V8016" "V8017" "V8018" "V8019"
## [8020] "V8020" "V8021" "V8022" "V8023" "V8024" "V8025" "V8026" "V8027" "V8028"
## [8029] "V8029" "V8030" "V8031" "V8032" "V8033" "V8034" "V8035" "V8036" "V8037"
## [8038] "V8038" "V8039" "V8040" "V8041" "V8042" "V8043" "V8044" "V8045" "V8046"
## [8047] "V8047" "V8048" "V8049" "V8050" "V8051" "V8052" "V8053" "V8054" "V8055"
## [8056] "V8056" "V8057" "V8058" "V8059" "V8060" "V8061" "V8062" "V8063" "V8064"
## [8065] "V8065" "V8066" "V8067" "V8068" "V8069" "V8070" "V8071" "V8072" "V8073"
## [8074] "V8074" "V8075" "V8076" "V8077" "V8078" "V8079" "V8080" "V8081" "V8082"
## [8083] "V8083" "V8084" "V8085" "V8086" "V8087" "V8088" "V8089" "V8090" "V8091"
## [8092] "V8092" "V8093" "V8094" "V8095" "V8096" "V8097" "V8098" "V8099" "V8100"
## [8101] "V8101" "V8102" "V8103" "V8104" "V8105" "V8106" "V8107" "V8108" "V8109"
## [8110] "V8110" "V8111" "V8112" "V8113" "V8114" "V8115" "V8116" "V8117" "V8118"
## [8119] "V8119" "V8120" "V8121" "V8122" "V8123" "V8124" "V8125" "V8126" "V8127"
## [8128] "V8128" "V8129" "V8130" "V8131" "V8132" "V8133" "V8134" "V8135" "V8136"
## [8137] "V8137" "V8138" "V8139" "V8140" "V8141" "V8142" "V8143" "V8144" "V8145"
## [8146] "V8146" "V8147" "V8148" "V8149" "V8150" "V8151" "V8152" "V8153" "V8154"
## [8155] "V8155" "V8156" "V8157" "V8158" "V8159" "V8160" "V8161" "V8162" "V8163"
## [8164] "V8164" "V8165" "V8166" "V8167" "V8168" "V8169" "V8170" "V8171" "V8172"
## [8173] "V8173" "V8174" "V8175" "V8176" "V8177" "V8178" "V8179" "V8180" "V8181"
## [8182] "V8182" "V8183" "V8184" "V8185" "V8186" "V8187" "V8188" "V8189" "V8190"
## [8191] "V8191" "V8192" "V8193" "V8194" "V8195" "V8196" "V8197" "V8198" "V8199"
## [8200] "V8200" "V8201" "V8202" "V8203" "V8204" "V8205" "V8206" "V8207" "V8208"
## [8209] "V8209" "V8210" "V8211" "V8212" "V8213" "V8214" "V8215" "V8216" "V8217"
## [8218] "V8218" "V8219" "V8220" "V8221" "V8222" "V8223" "V8224" "V8225" "V8226"
## [8227] "V8227" "V8228" "V8229" "V8230" "V8231" "V8232" "V8233" "V8234" "V8235"
## [8236] "V8236" "V8237" "V8238" "V8239" "V8240" "V8241" "V8242" "V8243" "V8244"
## [8245] "V8245" "V8246" "V8247" "V8248" "V8249" "V8250" "V8251" "V8252" "V8253"
## [8254] "V8254" "V8255" "V8256" "V8257" "V8258" "V8259" "V8260" "V8261" "V8262"
## [8263] "V8263" "V8264" "V8265" "V8266" "V8267" "V8268" "V8269" "V8270" "V8271"
## [8272] "V8272" "V8273" "V8274" "V8275" "V8276" "V8277" "V8278" "V8279" "V8280"
## [8281] "V8281" "V8282" "V8283" "V8284" "V8285" "V8286" "V8287" "V8288" "V8289"
## [8290] "V8290" "V8291" "V8292" "V8293" "V8294" "V8295" "V8296" "V8297" "V8298"
## [8299] "V8299" "V8300" "V8301" "V8302" "V8303" "V8304" "V8305" "V8306" "V8307"
## [8308] "V8308" "V8309" "V8310" "V8311" "V8312" "V8313" "V8314" "V8315" "V8316"
## [8317] "V8317" "V8318" "V8319" "V8320" "V8321" "V8322" "V8323" "V8324" "V8325"
## [8326] "V8326" "V8327" "V8328" "V8329" "V8330" "V8331" "V8332" "V8333" "V8334"
## [8335] "V8335" "V8336" "V8337" "V8338" "V8339" "V8340" "V8341" "V8342" "V8343"
## [8344] "V8344" "V8345" "V8346" "V8347" "V8348" "V8349" "V8350" "V8351" "V8352"
## [8353] "V8353" "V8354" "V8355" "V8356" "V8357" "V8358" "V8359" "V8360" "V8361"
## [8362] "V8362" "V8363" "V8364" "V8365" "V8366" "V8367" "V8368" "V8369" "V8370"
## [8371] "V8371" "V8372" "V8373" "V8374" "V8375" "V8376" "V8377" "V8378" "V8379"
## [8380] "V8380" "V8381" "V8382" "V8383" "V8384" "V8385" "V8386" "V8387" "V8388"
## [8389] "V8389" "V8390" "V8391" "V8392" "V8393" "V8394" "V8395" "V8396" "V8397"
## [8398] "V8398" "V8399" "V8400"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200# las filas son el número de puntos de evaluación de la función y las columnas el número de curvas
# Suavizamiento spline
# Creación de la base de funciones
npuntos <- nrow(imagenesEntxeyMeanFotodf)
base_bspline <- create.bspline.basis(c(1, npuntos), 150)
plot(base_bspline)
summary(base_bspline)##
## Basis object:
##
## Type: bspline
##
## Range: 1 to 200
##
## Number of basis functions: 150# Selección de lambda
gcv <- NULL
lambda_list <- seq(-2, 4, length.out = 30)
# estos son las potencias de 10 de variación del lambda
# inicia con valores muy cercanos a cero 10^-2 hasta 10^4
matrix_data <- as.matrix(imagenesEntxeyMeanFotodf)
for (lambda in lambda_list) {
lambda <- 10**lambda
param_lambda <- fdPar(base_bspline,
2,
lambda)
fd_smoothEnt <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
gcv <- c(gcv, sum(fd_smoothEnt$gcv))
}
# Se esta tomando como criterio total la suma de las VC de todas las curvas
plotgcv <-
data.frame(log_lambda = lambda_list,
gcv = gcv) %>% ggplot(aes(x = log_lambda, y = gcv)) +
geom_line(color = "purple", linetype = "dashed") +
theme_light() +
scale_x_continuous(limits = c(0, 4))
plotly::ggplotly(plotgcv)selected_lamdba <- 10**lambda_list[which.min(gcv)]
print(selected_lamdba)## [1] 0.02592944print(log(selected_lamdba, 10))## [1] -1.586207# entrega el valor de lambda donde se minimiza la suma de VC y log de lambda
# Suavizamiento con el lambda elegido
param_lambda <- fdPar(base_bspline,
2,
lambda = selected_lamdba)
fd_smoothEntMeanFoto <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
plot(fd_smoothEntMeanFoto, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(fd_smoothEntMeanFoto$fd[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
fd_fittedEntMeanFoto <- fitted(fd_smoothEntMeanFoto)
plot(imagenesEntxeyMeanFoto[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedEntMeanFoto[,1],col="2")
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))
attributes(imagenesEnsxeyMeanFoto)## $dim
## [1] 200 3600# Convertir en Data Frame Ensayo
imagenesEnsxeyMeanFotodf <- as.data.frame (imagenesEnsxeyMeanFoto)
attributes(imagenesEnsxeyMeanFotodf)## $names
## [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9"
## [10] "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18"
## [19] "V19" "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27"
## [28] "V28" "V29" "V30" "V31" "V32" "V33" "V34" "V35" "V36"
## [37] "V37" "V38" "V39" "V40" "V41" "V42" "V43" "V44" "V45"
## [46] "V46" "V47" "V48" "V49" "V50" "V51" "V52" "V53" "V54"
## [55] "V55" "V56" "V57" "V58" "V59" "V60" "V61" "V62" "V63"
## [64] "V64" "V65" "V66" "V67" "V68" "V69" "V70" "V71" "V72"
## [73] "V73" "V74" "V75" "V76" "V77" "V78" "V79" "V80" "V81"
## [82] "V82" "V83" "V84" "V85" "V86" "V87" "V88" "V89" "V90"
## [91] "V91" "V92" "V93" "V94" "V95" "V96" "V97" "V98" "V99"
## [100] "V100" "V101" "V102" "V103" "V104" "V105" "V106" "V107" "V108"
## [109] "V109" "V110" "V111" "V112" "V113" "V114" "V115" "V116" "V117"
## [118] "V118" "V119" "V120" "V121" "V122" "V123" "V124" "V125" "V126"
## [127] "V127" "V128" "V129" "V130" "V131" "V132" "V133" "V134" "V135"
## [136] "V136" "V137" "V138" "V139" "V140" "V141" "V142" "V143" "V144"
## [145] "V145" "V146" "V147" "V148" "V149" "V150" "V151" "V152" "V153"
## [154] "V154" "V155" "V156" "V157" "V158" "V159" "V160" "V161" "V162"
## [163] "V163" "V164" "V165" "V166" "V167" "V168" "V169" "V170" "V171"
## [172] "V172" "V173" "V174" "V175" "V176" "V177" "V178" "V179" "V180"
## [181] "V181" "V182" "V183" "V184" "V185" "V186" "V187" "V188" "V189"
## [190] "V190" "V191" "V192" "V193" "V194" "V195" "V196" "V197" "V198"
## [199] "V199" "V200" "V201" "V202" "V203" "V204" "V205" "V206" "V207"
## [208] "V208" "V209" "V210" "V211" "V212" "V213" "V214" "V215" "V216"
## [217] "V217" "V218" "V219" "V220" "V221" "V222" "V223" "V224" "V225"
## [226] "V226" "V227" "V228" "V229" "V230" "V231" "V232" "V233" "V234"
## [235] "V235" "V236" "V237" "V238" "V239" "V240" "V241" "V242" "V243"
## [244] "V244" "V245" "V246" "V247" "V248" "V249" "V250" "V251" "V252"
## [253] "V253" "V254" "V255" "V256" "V257" "V258" "V259" "V260" "V261"
## [262] "V262" "V263" "V264" "V265" "V266" "V267" "V268" "V269" "V270"
## [271] "V271" "V272" "V273" "V274" "V275" "V276" "V277" "V278" "V279"
## [280] "V280" "V281" "V282" "V283" "V284" "V285" "V286" "V287" "V288"
## [289] "V289" "V290" "V291" "V292" "V293" "V294" "V295" "V296" "V297"
## [298] "V298" "V299" "V300" "V301" "V302" "V303" "V304" "V305" "V306"
## [307] "V307" "V308" "V309" "V310" "V311" "V312" "V313" "V314" "V315"
## [316] "V316" "V317" "V318" "V319" "V320" "V321" "V322" "V323" "V324"
## [325] "V325" "V326" "V327" "V328" "V329" "V330" "V331" "V332" "V333"
## [334] "V334" "V335" "V336" "V337" "V338" "V339" "V340" "V341" "V342"
## [343] "V343" "V344" "V345" "V346" "V347" "V348" "V349" "V350" "V351"
## [352] "V352" "V353" "V354" "V355" "V356" "V357" "V358" "V359" "V360"
## [361] "V361" "V362" "V363" "V364" "V365" "V366" "V367" "V368" "V369"
## [370] "V370" "V371" "V372" "V373" "V374" "V375" "V376" "V377" "V378"
## [379] "V379" "V380" "V381" "V382" "V383" "V384" "V385" "V386" "V387"
## [388] "V388" "V389" "V390" "V391" "V392" "V393" "V394" "V395" "V396"
## [397] "V397" "V398" "V399" "V400" "V401" "V402" "V403" "V404" "V405"
## [406] "V406" "V407" "V408" "V409" "V410" "V411" "V412" "V413" "V414"
## [415] "V415" "V416" "V417" "V418" "V419" "V420" "V421" "V422" "V423"
## [424] "V424" "V425" "V426" "V427" "V428" "V429" "V430" "V431" "V432"
## [433] "V433" "V434" "V435" "V436" "V437" "V438" "V439" "V440" "V441"
## [442] "V442" "V443" "V444" "V445" "V446" "V447" "V448" "V449" "V450"
## [451] "V451" "V452" "V453" "V454" "V455" "V456" "V457" "V458" "V459"
## [460] "V460" "V461" "V462" "V463" "V464" "V465" "V466" "V467" "V468"
## [469] "V469" "V470" "V471" "V472" "V473" "V474" "V475" "V476" "V477"
## [478] "V478" "V479" "V480" "V481" "V482" "V483" "V484" "V485" "V486"
## [487] "V487" "V488" "V489" "V490" "V491" "V492" "V493" "V494" "V495"
## [496] "V496" "V497" "V498" "V499" "V500" "V501" "V502" "V503" "V504"
## [505] "V505" "V506" "V507" "V508" "V509" "V510" "V511" "V512" "V513"
## [514] "V514" "V515" "V516" "V517" "V518" "V519" "V520" "V521" "V522"
## [523] "V523" "V524" "V525" "V526" "V527" "V528" "V529" "V530" "V531"
## [532] "V532" "V533" "V534" "V535" "V536" "V537" "V538" "V539" "V540"
## [541] "V541" "V542" "V543" "V544" "V545" "V546" "V547" "V548" "V549"
## [550] "V550" "V551" "V552" "V553" "V554" "V555" "V556" "V557" "V558"
## [559] "V559" "V560" "V561" "V562" "V563" "V564" "V565" "V566" "V567"
## [568] "V568" "V569" "V570" "V571" "V572" "V573" "V574" "V575" "V576"
## [577] "V577" "V578" "V579" "V580" "V581" "V582" "V583" "V584" "V585"
## [586] "V586" "V587" "V588" "V589" "V590" "V591" "V592" "V593" "V594"
## [595] "V595" "V596" "V597" "V598" "V599" "V600" "V601" "V602" "V603"
## [604] "V604" "V605" "V606" "V607" "V608" "V609" "V610" "V611" "V612"
## [613] "V613" "V614" "V615" "V616" "V617" "V618" "V619" "V620" "V621"
## [622] "V622" "V623" "V624" "V625" "V626" "V627" "V628" "V629" "V630"
## [631] "V631" "V632" "V633" "V634" "V635" "V636" "V637" "V638" "V639"
## [640] "V640" "V641" "V642" "V643" "V644" "V645" "V646" "V647" "V648"
## [649] "V649" "V650" "V651" "V652" "V653" "V654" "V655" "V656" "V657"
## [658] "V658" "V659" "V660" "V661" "V662" "V663" "V664" "V665" "V666"
## [667] "V667" "V668" "V669" "V670" "V671" "V672" "V673" "V674" "V675"
## [676] "V676" "V677" "V678" "V679" "V680" "V681" "V682" "V683" "V684"
## [685] "V685" "V686" "V687" "V688" "V689" "V690" "V691" "V692" "V693"
## [694] "V694" "V695" "V696" "V697" "V698" "V699" "V700" "V701" "V702"
## [703] "V703" "V704" "V705" "V706" "V707" "V708" "V709" "V710" "V711"
## [712] "V712" "V713" "V714" "V715" "V716" "V717" "V718" "V719" "V720"
## [721] "V721" "V722" "V723" "V724" "V725" "V726" "V727" "V728" "V729"
## [730] "V730" "V731" "V732" "V733" "V734" "V735" "V736" "V737" "V738"
## [739] "V739" "V740" "V741" "V742" "V743" "V744" "V745" "V746" "V747"
## [748] "V748" "V749" "V750" "V751" "V752" "V753" "V754" "V755" "V756"
## [757] "V757" "V758" "V759" "V760" "V761" "V762" "V763" "V764" "V765"
## [766] "V766" "V767" "V768" "V769" "V770" "V771" "V772" "V773" "V774"
## [775] "V775" "V776" "V777" "V778" "V779" "V780" "V781" "V782" "V783"
## [784] "V784" "V785" "V786" "V787" "V788" "V789" "V790" "V791" "V792"
## [793] "V793" "V794" "V795" "V796" "V797" "V798" "V799" "V800" "V801"
## [802] "V802" "V803" "V804" "V805" "V806" "V807" "V808" "V809" "V810"
## [811] "V811" "V812" "V813" "V814" "V815" "V816" "V817" "V818" "V819"
## [820] "V820" "V821" "V822" "V823" "V824" "V825" "V826" "V827" "V828"
## [829] "V829" "V830" "V831" "V832" "V833" "V834" "V835" "V836" "V837"
## [838] "V838" "V839" "V840" "V841" "V842" "V843" "V844" "V845" "V846"
## [847] "V847" "V848" "V849" "V850" "V851" "V852" "V853" "V854" "V855"
## [856] "V856" "V857" "V858" "V859" "V860" "V861" "V862" "V863" "V864"
## [865] "V865" "V866" "V867" "V868" "V869" "V870" "V871" "V872" "V873"
## [874] "V874" "V875" "V876" "V877" "V878" "V879" "V880" "V881" "V882"
## [883] "V883" "V884" "V885" "V886" "V887" "V888" "V889" "V890" "V891"
## [892] "V892" "V893" "V894" "V895" "V896" "V897" "V898" "V899" "V900"
## [901] "V901" "V902" "V903" "V904" "V905" "V906" "V907" "V908" "V909"
## [910] "V910" "V911" "V912" "V913" "V914" "V915" "V916" "V917" "V918"
## [919] "V919" "V920" "V921" "V922" "V923" "V924" "V925" "V926" "V927"
## [928] "V928" "V929" "V930" "V931" "V932" "V933" "V934" "V935" "V936"
## [937] "V937" "V938" "V939" "V940" "V941" "V942" "V943" "V944" "V945"
## [946] "V946" "V947" "V948" "V949" "V950" "V951" "V952" "V953" "V954"
## [955] "V955" "V956" "V957" "V958" "V959" "V960" "V961" "V962" "V963"
## [964] "V964" "V965" "V966" "V967" "V968" "V969" "V970" "V971" "V972"
## [973] "V973" "V974" "V975" "V976" "V977" "V978" "V979" "V980" "V981"
## [982] "V982" "V983" "V984" "V985" "V986" "V987" "V988" "V989" "V990"
## [991] "V991" "V992" "V993" "V994" "V995" "V996" "V997" "V998" "V999"
## [1000] "V1000" "V1001" "V1002" "V1003" "V1004" "V1005" "V1006" "V1007" "V1008"
## [1009] "V1009" "V1010" "V1011" "V1012" "V1013" "V1014" "V1015" "V1016" "V1017"
## [1018] "V1018" "V1019" "V1020" "V1021" "V1022" "V1023" "V1024" "V1025" "V1026"
## [1027] "V1027" "V1028" "V1029" "V1030" "V1031" "V1032" "V1033" "V1034" "V1035"
## [1036] "V1036" "V1037" "V1038" "V1039" "V1040" "V1041" "V1042" "V1043" "V1044"
## [1045] "V1045" "V1046" "V1047" "V1048" "V1049" "V1050" "V1051" "V1052" "V1053"
## [1054] "V1054" "V1055" "V1056" "V1057" "V1058" "V1059" "V1060" "V1061" "V1062"
## [1063] "V1063" "V1064" "V1065" "V1066" "V1067" "V1068" "V1069" "V1070" "V1071"
## [1072] "V1072" "V1073" "V1074" "V1075" "V1076" "V1077" "V1078" "V1079" "V1080"
## [1081] "V1081" "V1082" "V1083" "V1084" "V1085" "V1086" "V1087" "V1088" "V1089"
## [1090] "V1090" "V1091" "V1092" "V1093" "V1094" "V1095" "V1096" "V1097" "V1098"
## [1099] "V1099" "V1100" "V1101" "V1102" "V1103" "V1104" "V1105" "V1106" "V1107"
## [1108] "V1108" "V1109" "V1110" "V1111" "V1112" "V1113" "V1114" "V1115" "V1116"
## [1117] "V1117" "V1118" "V1119" "V1120" "V1121" "V1122" "V1123" "V1124" "V1125"
## [1126] "V1126" "V1127" "V1128" "V1129" "V1130" "V1131" "V1132" "V1133" "V1134"
## [1135] "V1135" "V1136" "V1137" "V1138" "V1139" "V1140" "V1141" "V1142" "V1143"
## [1144] "V1144" "V1145" "V1146" "V1147" "V1148" "V1149" "V1150" "V1151" "V1152"
## [1153] "V1153" "V1154" "V1155" "V1156" "V1157" "V1158" "V1159" "V1160" "V1161"
## [1162] "V1162" "V1163" "V1164" "V1165" "V1166" "V1167" "V1168" "V1169" "V1170"
## [1171] "V1171" "V1172" "V1173" "V1174" "V1175" "V1176" "V1177" "V1178" "V1179"
## [1180] "V1180" "V1181" "V1182" "V1183" "V1184" "V1185" "V1186" "V1187" "V1188"
## [1189] "V1189" "V1190" "V1191" "V1192" "V1193" "V1194" "V1195" "V1196" "V1197"
## [1198] "V1198" "V1199" "V1200" "V1201" "V1202" "V1203" "V1204" "V1205" "V1206"
## [1207] "V1207" "V1208" "V1209" "V1210" "V1211" "V1212" "V1213" "V1214" "V1215"
## [1216] "V1216" "V1217" "V1218" "V1219" "V1220" "V1221" "V1222" "V1223" "V1224"
## [1225] "V1225" "V1226" "V1227" "V1228" "V1229" "V1230" "V1231" "V1232" "V1233"
## [1234] "V1234" "V1235" "V1236" "V1237" "V1238" "V1239" "V1240" "V1241" "V1242"
## [1243] "V1243" "V1244" "V1245" "V1246" "V1247" "V1248" "V1249" "V1250" "V1251"
## [1252] "V1252" "V1253" "V1254" "V1255" "V1256" "V1257" "V1258" "V1259" "V1260"
## [1261] "V1261" "V1262" "V1263" "V1264" "V1265" "V1266" "V1267" "V1268" "V1269"
## [1270] "V1270" "V1271" "V1272" "V1273" "V1274" "V1275" "V1276" "V1277" "V1278"
## [1279] "V1279" "V1280" "V1281" "V1282" "V1283" "V1284" "V1285" "V1286" "V1287"
## [1288] "V1288" "V1289" "V1290" "V1291" "V1292" "V1293" "V1294" "V1295" "V1296"
## [1297] "V1297" "V1298" "V1299" "V1300" "V1301" "V1302" "V1303" "V1304" "V1305"
## [1306] "V1306" "V1307" "V1308" "V1309" "V1310" "V1311" "V1312" "V1313" "V1314"
## [1315] "V1315" "V1316" "V1317" "V1318" "V1319" "V1320" "V1321" "V1322" "V1323"
## [1324] "V1324" "V1325" "V1326" "V1327" "V1328" "V1329" "V1330" "V1331" "V1332"
## [1333] "V1333" "V1334" "V1335" "V1336" "V1337" "V1338" "V1339" "V1340" "V1341"
## [1342] "V1342" "V1343" "V1344" "V1345" "V1346" "V1347" "V1348" "V1349" "V1350"
## [1351] "V1351" "V1352" "V1353" "V1354" "V1355" "V1356" "V1357" "V1358" "V1359"
## [1360] "V1360" "V1361" "V1362" "V1363" "V1364" "V1365" "V1366" "V1367" "V1368"
## [1369] "V1369" "V1370" "V1371" "V1372" "V1373" "V1374" "V1375" "V1376" "V1377"
## [1378] "V1378" "V1379" "V1380" "V1381" "V1382" "V1383" "V1384" "V1385" "V1386"
## [1387] "V1387" "V1388" "V1389" "V1390" "V1391" "V1392" "V1393" "V1394" "V1395"
## [1396] "V1396" "V1397" "V1398" "V1399" "V1400" "V1401" "V1402" "V1403" "V1404"
## [1405] "V1405" "V1406" "V1407" "V1408" "V1409" "V1410" "V1411" "V1412" "V1413"
## [1414] "V1414" "V1415" "V1416" "V1417" "V1418" "V1419" "V1420" "V1421" "V1422"
## [1423] "V1423" "V1424" "V1425" "V1426" "V1427" "V1428" "V1429" "V1430" "V1431"
## [1432] "V1432" "V1433" "V1434" "V1435" "V1436" "V1437" "V1438" "V1439" "V1440"
## [1441] "V1441" "V1442" "V1443" "V1444" "V1445" "V1446" "V1447" "V1448" "V1449"
## [1450] "V1450" "V1451" "V1452" "V1453" "V1454" "V1455" "V1456" "V1457" "V1458"
## [1459] "V1459" "V1460" "V1461" "V1462" "V1463" "V1464" "V1465" "V1466" "V1467"
## [1468] "V1468" "V1469" "V1470" "V1471" "V1472" "V1473" "V1474" "V1475" "V1476"
## [1477] "V1477" "V1478" "V1479" "V1480" "V1481" "V1482" "V1483" "V1484" "V1485"
## [1486] "V1486" "V1487" "V1488" "V1489" "V1490" "V1491" "V1492" "V1493" "V1494"
## [1495] "V1495" "V1496" "V1497" "V1498" "V1499" "V1500" "V1501" "V1502" "V1503"
## [1504] "V1504" "V1505" "V1506" "V1507" "V1508" "V1509" "V1510" "V1511" "V1512"
## [1513] "V1513" "V1514" "V1515" "V1516" "V1517" "V1518" "V1519" "V1520" "V1521"
## [1522] "V1522" "V1523" "V1524" "V1525" "V1526" "V1527" "V1528" "V1529" "V1530"
## [1531] "V1531" "V1532" "V1533" "V1534" "V1535" "V1536" "V1537" "V1538" "V1539"
## [1540] "V1540" "V1541" "V1542" "V1543" "V1544" "V1545" "V1546" "V1547" "V1548"
## [1549] "V1549" "V1550" "V1551" "V1552" "V1553" "V1554" "V1555" "V1556" "V1557"
## [1558] "V1558" "V1559" "V1560" "V1561" "V1562" "V1563" "V1564" "V1565" "V1566"
## [1567] "V1567" "V1568" "V1569" "V1570" "V1571" "V1572" "V1573" "V1574" "V1575"
## [1576] "V1576" "V1577" "V1578" "V1579" "V1580" "V1581" "V1582" "V1583" "V1584"
## [1585] "V1585" "V1586" "V1587" "V1588" "V1589" "V1590" "V1591" "V1592" "V1593"
## [1594] "V1594" "V1595" "V1596" "V1597" "V1598" "V1599" "V1600" "V1601" "V1602"
## [1603] "V1603" "V1604" "V1605" "V1606" "V1607" "V1608" "V1609" "V1610" "V1611"
## [1612] "V1612" "V1613" "V1614" "V1615" "V1616" "V1617" "V1618" "V1619" "V1620"
## [1621] "V1621" "V1622" "V1623" "V1624" "V1625" "V1626" "V1627" "V1628" "V1629"
## [1630] "V1630" "V1631" "V1632" "V1633" "V1634" "V1635" "V1636" "V1637" "V1638"
## [1639] "V1639" "V1640" "V1641" "V1642" "V1643" "V1644" "V1645" "V1646" "V1647"
## [1648] "V1648" "V1649" "V1650" "V1651" "V1652" "V1653" "V1654" "V1655" "V1656"
## [1657] "V1657" "V1658" "V1659" "V1660" "V1661" "V1662" "V1663" "V1664" "V1665"
## [1666] "V1666" "V1667" "V1668" "V1669" "V1670" "V1671" "V1672" "V1673" "V1674"
## [1675] "V1675" "V1676" "V1677" "V1678" "V1679" "V1680" "V1681" "V1682" "V1683"
## [1684] "V1684" "V1685" "V1686" "V1687" "V1688" "V1689" "V1690" "V1691" "V1692"
## [1693] "V1693" "V1694" "V1695" "V1696" "V1697" "V1698" "V1699" "V1700" "V1701"
## [1702] "V1702" "V1703" "V1704" "V1705" "V1706" "V1707" "V1708" "V1709" "V1710"
## [1711] "V1711" "V1712" "V1713" "V1714" "V1715" "V1716" "V1717" "V1718" "V1719"
## [1720] "V1720" "V1721" "V1722" "V1723" "V1724" "V1725" "V1726" "V1727" "V1728"
## [1729] "V1729" "V1730" "V1731" "V1732" "V1733" "V1734" "V1735" "V1736" "V1737"
## [1738] "V1738" "V1739" "V1740" "V1741" "V1742" "V1743" "V1744" "V1745" "V1746"
## [1747] "V1747" "V1748" "V1749" "V1750" "V1751" "V1752" "V1753" "V1754" "V1755"
## [1756] "V1756" "V1757" "V1758" "V1759" "V1760" "V1761" "V1762" "V1763" "V1764"
## [1765] "V1765" "V1766" "V1767" "V1768" "V1769" "V1770" "V1771" "V1772" "V1773"
## [1774] "V1774" "V1775" "V1776" "V1777" "V1778" "V1779" "V1780" "V1781" "V1782"
## [1783] "V1783" "V1784" "V1785" "V1786" "V1787" "V1788" "V1789" "V1790" "V1791"
## [1792] "V1792" "V1793" "V1794" "V1795" "V1796" "V1797" "V1798" "V1799" "V1800"
## [1801] "V1801" "V1802" "V1803" "V1804" "V1805" "V1806" "V1807" "V1808" "V1809"
## [1810] "V1810" "V1811" "V1812" "V1813" "V1814" "V1815" "V1816" "V1817" "V1818"
## [1819] "V1819" "V1820" "V1821" "V1822" "V1823" "V1824" "V1825" "V1826" "V1827"
## [1828] "V1828" "V1829" "V1830" "V1831" "V1832" "V1833" "V1834" "V1835" "V1836"
## [1837] "V1837" "V1838" "V1839" "V1840" "V1841" "V1842" "V1843" "V1844" "V1845"
## [1846] "V1846" "V1847" "V1848" "V1849" "V1850" "V1851" "V1852" "V1853" "V1854"
## [1855] "V1855" "V1856" "V1857" "V1858" "V1859" "V1860" "V1861" "V1862" "V1863"
## [1864] "V1864" "V1865" "V1866" "V1867" "V1868" "V1869" "V1870" "V1871" "V1872"
## [1873] "V1873" "V1874" "V1875" "V1876" "V1877" "V1878" "V1879" "V1880" "V1881"
## [1882] "V1882" "V1883" "V1884" "V1885" "V1886" "V1887" "V1888" "V1889" "V1890"
## [1891] "V1891" "V1892" "V1893" "V1894" "V1895" "V1896" "V1897" "V1898" "V1899"
## [1900] "V1900" "V1901" "V1902" "V1903" "V1904" "V1905" "V1906" "V1907" "V1908"
## [1909] "V1909" "V1910" "V1911" "V1912" "V1913" "V1914" "V1915" "V1916" "V1917"
## [1918] "V1918" "V1919" "V1920" "V1921" "V1922" "V1923" "V1924" "V1925" "V1926"
## [1927] "V1927" "V1928" "V1929" "V1930" "V1931" "V1932" "V1933" "V1934" "V1935"
## [1936] "V1936" "V1937" "V1938" "V1939" "V1940" "V1941" "V1942" "V1943" "V1944"
## [1945] "V1945" "V1946" "V1947" "V1948" "V1949" "V1950" "V1951" "V1952" "V1953"
## [1954] "V1954" "V1955" "V1956" "V1957" "V1958" "V1959" "V1960" "V1961" "V1962"
## [1963] "V1963" "V1964" "V1965" "V1966" "V1967" "V1968" "V1969" "V1970" "V1971"
## [1972] "V1972" "V1973" "V1974" "V1975" "V1976" "V1977" "V1978" "V1979" "V1980"
## [1981] "V1981" "V1982" "V1983" "V1984" "V1985" "V1986" "V1987" "V1988" "V1989"
## [1990] "V1990" "V1991" "V1992" "V1993" "V1994" "V1995" "V1996" "V1997" "V1998"
## [1999] "V1999" "V2000" "V2001" "V2002" "V2003" "V2004" "V2005" "V2006" "V2007"
## [2008] "V2008" "V2009" "V2010" "V2011" "V2012" "V2013" "V2014" "V2015" "V2016"
## [2017] "V2017" "V2018" "V2019" "V2020" "V2021" "V2022" "V2023" "V2024" "V2025"
## [2026] "V2026" "V2027" "V2028" "V2029" "V2030" "V2031" "V2032" "V2033" "V2034"
## [2035] "V2035" "V2036" "V2037" "V2038" "V2039" "V2040" "V2041" "V2042" "V2043"
## [2044] "V2044" "V2045" "V2046" "V2047" "V2048" "V2049" "V2050" "V2051" "V2052"
## [2053] "V2053" "V2054" "V2055" "V2056" "V2057" "V2058" "V2059" "V2060" "V2061"
## [2062] "V2062" "V2063" "V2064" "V2065" "V2066" "V2067" "V2068" "V2069" "V2070"
## [2071] "V2071" "V2072" "V2073" "V2074" "V2075" "V2076" "V2077" "V2078" "V2079"
## [2080] "V2080" "V2081" "V2082" "V2083" "V2084" "V2085" "V2086" "V2087" "V2088"
## [2089] "V2089" "V2090" "V2091" "V2092" "V2093" "V2094" "V2095" "V2096" "V2097"
## [2098] "V2098" "V2099" "V2100" "V2101" "V2102" "V2103" "V2104" "V2105" "V2106"
## [2107] "V2107" "V2108" "V2109" "V2110" "V2111" "V2112" "V2113" "V2114" "V2115"
## [2116] "V2116" "V2117" "V2118" "V2119" "V2120" "V2121" "V2122" "V2123" "V2124"
## [2125] "V2125" "V2126" "V2127" "V2128" "V2129" "V2130" "V2131" "V2132" "V2133"
## [2134] "V2134" "V2135" "V2136" "V2137" "V2138" "V2139" "V2140" "V2141" "V2142"
## [2143] "V2143" "V2144" "V2145" "V2146" "V2147" "V2148" "V2149" "V2150" "V2151"
## [2152] "V2152" "V2153" "V2154" "V2155" "V2156" "V2157" "V2158" "V2159" "V2160"
## [2161] "V2161" "V2162" "V2163" "V2164" "V2165" "V2166" "V2167" "V2168" "V2169"
## [2170] "V2170" "V2171" "V2172" "V2173" "V2174" "V2175" "V2176" "V2177" "V2178"
## [2179] "V2179" "V2180" "V2181" "V2182" "V2183" "V2184" "V2185" "V2186" "V2187"
## [2188] "V2188" "V2189" "V2190" "V2191" "V2192" "V2193" "V2194" "V2195" "V2196"
## [2197] "V2197" "V2198" "V2199" "V2200" "V2201" "V2202" "V2203" "V2204" "V2205"
## [2206] "V2206" "V2207" "V2208" "V2209" "V2210" "V2211" "V2212" "V2213" "V2214"
## [2215] "V2215" "V2216" "V2217" "V2218" "V2219" "V2220" "V2221" "V2222" "V2223"
## [2224] "V2224" "V2225" "V2226" "V2227" "V2228" "V2229" "V2230" "V2231" "V2232"
## [2233] "V2233" "V2234" "V2235" "V2236" "V2237" "V2238" "V2239" "V2240" "V2241"
## [2242] "V2242" "V2243" "V2244" "V2245" "V2246" "V2247" "V2248" "V2249" "V2250"
## [2251] "V2251" "V2252" "V2253" "V2254" "V2255" "V2256" "V2257" "V2258" "V2259"
## [2260] "V2260" "V2261" "V2262" "V2263" "V2264" "V2265" "V2266" "V2267" "V2268"
## [2269] "V2269" "V2270" "V2271" "V2272" "V2273" "V2274" "V2275" "V2276" "V2277"
## [2278] "V2278" "V2279" "V2280" "V2281" "V2282" "V2283" "V2284" "V2285" "V2286"
## [2287] "V2287" "V2288" "V2289" "V2290" "V2291" "V2292" "V2293" "V2294" "V2295"
## [2296] "V2296" "V2297" "V2298" "V2299" "V2300" "V2301" "V2302" "V2303" "V2304"
## [2305] "V2305" "V2306" "V2307" "V2308" "V2309" "V2310" "V2311" "V2312" "V2313"
## [2314] "V2314" "V2315" "V2316" "V2317" "V2318" "V2319" "V2320" "V2321" "V2322"
## [2323] "V2323" "V2324" "V2325" "V2326" "V2327" "V2328" "V2329" "V2330" "V2331"
## [2332] "V2332" "V2333" "V2334" "V2335" "V2336" "V2337" "V2338" "V2339" "V2340"
## [2341] "V2341" "V2342" "V2343" "V2344" "V2345" "V2346" "V2347" "V2348" "V2349"
## [2350] "V2350" "V2351" "V2352" "V2353" "V2354" "V2355" "V2356" "V2357" "V2358"
## [2359] "V2359" "V2360" "V2361" "V2362" "V2363" "V2364" "V2365" "V2366" "V2367"
## [2368] "V2368" "V2369" "V2370" "V2371" "V2372" "V2373" "V2374" "V2375" "V2376"
## [2377] "V2377" "V2378" "V2379" "V2380" "V2381" "V2382" "V2383" "V2384" "V2385"
## [2386] "V2386" "V2387" "V2388" "V2389" "V2390" "V2391" "V2392" "V2393" "V2394"
## [2395] "V2395" "V2396" "V2397" "V2398" "V2399" "V2400" "V2401" "V2402" "V2403"
## [2404] "V2404" "V2405" "V2406" "V2407" "V2408" "V2409" "V2410" "V2411" "V2412"
## [2413] "V2413" "V2414" "V2415" "V2416" "V2417" "V2418" "V2419" "V2420" "V2421"
## [2422] "V2422" "V2423" "V2424" "V2425" "V2426" "V2427" "V2428" "V2429" "V2430"
## [2431] "V2431" "V2432" "V2433" "V2434" "V2435" "V2436" "V2437" "V2438" "V2439"
## [2440] "V2440" "V2441" "V2442" "V2443" "V2444" "V2445" "V2446" "V2447" "V2448"
## [2449] "V2449" "V2450" "V2451" "V2452" "V2453" "V2454" "V2455" "V2456" "V2457"
## [2458] "V2458" "V2459" "V2460" "V2461" "V2462" "V2463" "V2464" "V2465" "V2466"
## [2467] "V2467" "V2468" "V2469" "V2470" "V2471" "V2472" "V2473" "V2474" "V2475"
## [2476] "V2476" "V2477" "V2478" "V2479" "V2480" "V2481" "V2482" "V2483" "V2484"
## [2485] "V2485" "V2486" "V2487" "V2488" "V2489" "V2490" "V2491" "V2492" "V2493"
## [2494] "V2494" "V2495" "V2496" "V2497" "V2498" "V2499" "V2500" "V2501" "V2502"
## [2503] "V2503" "V2504" "V2505" "V2506" "V2507" "V2508" "V2509" "V2510" "V2511"
## [2512] "V2512" "V2513" "V2514" "V2515" "V2516" "V2517" "V2518" "V2519" "V2520"
## [2521] "V2521" "V2522" "V2523" "V2524" "V2525" "V2526" "V2527" "V2528" "V2529"
## [2530] "V2530" "V2531" "V2532" "V2533" "V2534" "V2535" "V2536" "V2537" "V2538"
## [2539] "V2539" "V2540" "V2541" "V2542" "V2543" "V2544" "V2545" "V2546" "V2547"
## [2548] "V2548" "V2549" "V2550" "V2551" "V2552" "V2553" "V2554" "V2555" "V2556"
## [2557] "V2557" "V2558" "V2559" "V2560" "V2561" "V2562" "V2563" "V2564" "V2565"
## [2566] "V2566" "V2567" "V2568" "V2569" "V2570" "V2571" "V2572" "V2573" "V2574"
## [2575] "V2575" "V2576" "V2577" "V2578" "V2579" "V2580" "V2581" "V2582" "V2583"
## [2584] "V2584" "V2585" "V2586" "V2587" "V2588" "V2589" "V2590" "V2591" "V2592"
## [2593] "V2593" "V2594" "V2595" "V2596" "V2597" "V2598" "V2599" "V2600" "V2601"
## [2602] "V2602" "V2603" "V2604" "V2605" "V2606" "V2607" "V2608" "V2609" "V2610"
## [2611] "V2611" "V2612" "V2613" "V2614" "V2615" "V2616" "V2617" "V2618" "V2619"
## [2620] "V2620" "V2621" "V2622" "V2623" "V2624" "V2625" "V2626" "V2627" "V2628"
## [2629] "V2629" "V2630" "V2631" "V2632" "V2633" "V2634" "V2635" "V2636" "V2637"
## [2638] "V2638" "V2639" "V2640" "V2641" "V2642" "V2643" "V2644" "V2645" "V2646"
## [2647] "V2647" "V2648" "V2649" "V2650" "V2651" "V2652" "V2653" "V2654" "V2655"
## [2656] "V2656" "V2657" "V2658" "V2659" "V2660" "V2661" "V2662" "V2663" "V2664"
## [2665] "V2665" "V2666" "V2667" "V2668" "V2669" "V2670" "V2671" "V2672" "V2673"
## [2674] "V2674" "V2675" "V2676" "V2677" "V2678" "V2679" "V2680" "V2681" "V2682"
## [2683] "V2683" "V2684" "V2685" "V2686" "V2687" "V2688" "V2689" "V2690" "V2691"
## [2692] "V2692" "V2693" "V2694" "V2695" "V2696" "V2697" "V2698" "V2699" "V2700"
## [2701] "V2701" "V2702" "V2703" "V2704" "V2705" "V2706" "V2707" "V2708" "V2709"
## [2710] "V2710" "V2711" "V2712" "V2713" "V2714" "V2715" "V2716" "V2717" "V2718"
## [2719] "V2719" "V2720" "V2721" "V2722" "V2723" "V2724" "V2725" "V2726" "V2727"
## [2728] "V2728" "V2729" "V2730" "V2731" "V2732" "V2733" "V2734" "V2735" "V2736"
## [2737] "V2737" "V2738" "V2739" "V2740" "V2741" "V2742" "V2743" "V2744" "V2745"
## [2746] "V2746" "V2747" "V2748" "V2749" "V2750" "V2751" "V2752" "V2753" "V2754"
## [2755] "V2755" "V2756" "V2757" "V2758" "V2759" "V2760" "V2761" "V2762" "V2763"
## [2764] "V2764" "V2765" "V2766" "V2767" "V2768" "V2769" "V2770" "V2771" "V2772"
## [2773] "V2773" "V2774" "V2775" "V2776" "V2777" "V2778" "V2779" "V2780" "V2781"
## [2782] "V2782" "V2783" "V2784" "V2785" "V2786" "V2787" "V2788" "V2789" "V2790"
## [2791] "V2791" "V2792" "V2793" "V2794" "V2795" "V2796" "V2797" "V2798" "V2799"
## [2800] "V2800" "V2801" "V2802" "V2803" "V2804" "V2805" "V2806" "V2807" "V2808"
## [2809] "V2809" "V2810" "V2811" "V2812" "V2813" "V2814" "V2815" "V2816" "V2817"
## [2818] "V2818" "V2819" "V2820" "V2821" "V2822" "V2823" "V2824" "V2825" "V2826"
## [2827] "V2827" "V2828" "V2829" "V2830" "V2831" "V2832" "V2833" "V2834" "V2835"
## [2836] "V2836" "V2837" "V2838" "V2839" "V2840" "V2841" "V2842" "V2843" "V2844"
## [2845] "V2845" "V2846" "V2847" "V2848" "V2849" "V2850" "V2851" "V2852" "V2853"
## [2854] "V2854" "V2855" "V2856" "V2857" "V2858" "V2859" "V2860" "V2861" "V2862"
## [2863] "V2863" "V2864" "V2865" "V2866" "V2867" "V2868" "V2869" "V2870" "V2871"
## [2872] "V2872" "V2873" "V2874" "V2875" "V2876" "V2877" "V2878" "V2879" "V2880"
## [2881] "V2881" "V2882" "V2883" "V2884" "V2885" "V2886" "V2887" "V2888" "V2889"
## [2890] "V2890" "V2891" "V2892" "V2893" "V2894" "V2895" "V2896" "V2897" "V2898"
## [2899] "V2899" "V2900" "V2901" "V2902" "V2903" "V2904" "V2905" "V2906" "V2907"
## [2908] "V2908" "V2909" "V2910" "V2911" "V2912" "V2913" "V2914" "V2915" "V2916"
## [2917] "V2917" "V2918" "V2919" "V2920" "V2921" "V2922" "V2923" "V2924" "V2925"
## [2926] "V2926" "V2927" "V2928" "V2929" "V2930" "V2931" "V2932" "V2933" "V2934"
## [2935] "V2935" "V2936" "V2937" "V2938" "V2939" "V2940" "V2941" "V2942" "V2943"
## [2944] "V2944" "V2945" "V2946" "V2947" "V2948" "V2949" "V2950" "V2951" "V2952"
## [2953] "V2953" "V2954" "V2955" "V2956" "V2957" "V2958" "V2959" "V2960" "V2961"
## [2962] "V2962" "V2963" "V2964" "V2965" "V2966" "V2967" "V2968" "V2969" "V2970"
## [2971] "V2971" "V2972" "V2973" "V2974" "V2975" "V2976" "V2977" "V2978" "V2979"
## [2980] "V2980" "V2981" "V2982" "V2983" "V2984" "V2985" "V2986" "V2987" "V2988"
## [2989] "V2989" "V2990" "V2991" "V2992" "V2993" "V2994" "V2995" "V2996" "V2997"
## [2998] "V2998" "V2999" "V3000" "V3001" "V3002" "V3003" "V3004" "V3005" "V3006"
## [3007] "V3007" "V3008" "V3009" "V3010" "V3011" "V3012" "V3013" "V3014" "V3015"
## [3016] "V3016" "V3017" "V3018" "V3019" "V3020" "V3021" "V3022" "V3023" "V3024"
## [3025] "V3025" "V3026" "V3027" "V3028" "V3029" "V3030" "V3031" "V3032" "V3033"
## [3034] "V3034" "V3035" "V3036" "V3037" "V3038" "V3039" "V3040" "V3041" "V3042"
## [3043] "V3043" "V3044" "V3045" "V3046" "V3047" "V3048" "V3049" "V3050" "V3051"
## [3052] "V3052" "V3053" "V3054" "V3055" "V3056" "V3057" "V3058" "V3059" "V3060"
## [3061] "V3061" "V3062" "V3063" "V3064" "V3065" "V3066" "V3067" "V3068" "V3069"
## [3070] "V3070" "V3071" "V3072" "V3073" "V3074" "V3075" "V3076" "V3077" "V3078"
## [3079] "V3079" "V3080" "V3081" "V3082" "V3083" "V3084" "V3085" "V3086" "V3087"
## [3088] "V3088" "V3089" "V3090" "V3091" "V3092" "V3093" "V3094" "V3095" "V3096"
## [3097] "V3097" "V3098" "V3099" "V3100" "V3101" "V3102" "V3103" "V3104" "V3105"
## [3106] "V3106" "V3107" "V3108" "V3109" "V3110" "V3111" "V3112" "V3113" "V3114"
## [3115] "V3115" "V3116" "V3117" "V3118" "V3119" "V3120" "V3121" "V3122" "V3123"
## [3124] "V3124" "V3125" "V3126" "V3127" "V3128" "V3129" "V3130" "V3131" "V3132"
## [3133] "V3133" "V3134" "V3135" "V3136" "V3137" "V3138" "V3139" "V3140" "V3141"
## [3142] "V3142" "V3143" "V3144" "V3145" "V3146" "V3147" "V3148" "V3149" "V3150"
## [3151] "V3151" "V3152" "V3153" "V3154" "V3155" "V3156" "V3157" "V3158" "V3159"
## [3160] "V3160" "V3161" "V3162" "V3163" "V3164" "V3165" "V3166" "V3167" "V3168"
## [3169] "V3169" "V3170" "V3171" "V3172" "V3173" "V3174" "V3175" "V3176" "V3177"
## [3178] "V3178" "V3179" "V3180" "V3181" "V3182" "V3183" "V3184" "V3185" "V3186"
## [3187] "V3187" "V3188" "V3189" "V3190" "V3191" "V3192" "V3193" "V3194" "V3195"
## [3196] "V3196" "V3197" "V3198" "V3199" "V3200" "V3201" "V3202" "V3203" "V3204"
## [3205] "V3205" "V3206" "V3207" "V3208" "V3209" "V3210" "V3211" "V3212" "V3213"
## [3214] "V3214" "V3215" "V3216" "V3217" "V3218" "V3219" "V3220" "V3221" "V3222"
## [3223] "V3223" "V3224" "V3225" "V3226" "V3227" "V3228" "V3229" "V3230" "V3231"
## [3232] "V3232" "V3233" "V3234" "V3235" "V3236" "V3237" "V3238" "V3239" "V3240"
## [3241] "V3241" "V3242" "V3243" "V3244" "V3245" "V3246" "V3247" "V3248" "V3249"
## [3250] "V3250" "V3251" "V3252" "V3253" "V3254" "V3255" "V3256" "V3257" "V3258"
## [3259] "V3259" "V3260" "V3261" "V3262" "V3263" "V3264" "V3265" "V3266" "V3267"
## [3268] "V3268" "V3269" "V3270" "V3271" "V3272" "V3273" "V3274" "V3275" "V3276"
## [3277] "V3277" "V3278" "V3279" "V3280" "V3281" "V3282" "V3283" "V3284" "V3285"
## [3286] "V3286" "V3287" "V3288" "V3289" "V3290" "V3291" "V3292" "V3293" "V3294"
## [3295] "V3295" "V3296" "V3297" "V3298" "V3299" "V3300" "V3301" "V3302" "V3303"
## [3304] "V3304" "V3305" "V3306" "V3307" "V3308" "V3309" "V3310" "V3311" "V3312"
## [3313] "V3313" "V3314" "V3315" "V3316" "V3317" "V3318" "V3319" "V3320" "V3321"
## [3322] "V3322" "V3323" "V3324" "V3325" "V3326" "V3327" "V3328" "V3329" "V3330"
## [3331] "V3331" "V3332" "V3333" "V3334" "V3335" "V3336" "V3337" "V3338" "V3339"
## [3340] "V3340" "V3341" "V3342" "V3343" "V3344" "V3345" "V3346" "V3347" "V3348"
## [3349] "V3349" "V3350" "V3351" "V3352" "V3353" "V3354" "V3355" "V3356" "V3357"
## [3358] "V3358" "V3359" "V3360" "V3361" "V3362" "V3363" "V3364" "V3365" "V3366"
## [3367] "V3367" "V3368" "V3369" "V3370" "V3371" "V3372" "V3373" "V3374" "V3375"
## [3376] "V3376" "V3377" "V3378" "V3379" "V3380" "V3381" "V3382" "V3383" "V3384"
## [3385] "V3385" "V3386" "V3387" "V3388" "V3389" "V3390" "V3391" "V3392" "V3393"
## [3394] "V3394" "V3395" "V3396" "V3397" "V3398" "V3399" "V3400" "V3401" "V3402"
## [3403] "V3403" "V3404" "V3405" "V3406" "V3407" "V3408" "V3409" "V3410" "V3411"
## [3412] "V3412" "V3413" "V3414" "V3415" "V3416" "V3417" "V3418" "V3419" "V3420"
## [3421] "V3421" "V3422" "V3423" "V3424" "V3425" "V3426" "V3427" "V3428" "V3429"
## [3430] "V3430" "V3431" "V3432" "V3433" "V3434" "V3435" "V3436" "V3437" "V3438"
## [3439] "V3439" "V3440" "V3441" "V3442" "V3443" "V3444" "V3445" "V3446" "V3447"
## [3448] "V3448" "V3449" "V3450" "V3451" "V3452" "V3453" "V3454" "V3455" "V3456"
## [3457] "V3457" "V3458" "V3459" "V3460" "V3461" "V3462" "V3463" "V3464" "V3465"
## [3466] "V3466" "V3467" "V3468" "V3469" "V3470" "V3471" "V3472" "V3473" "V3474"
## [3475] "V3475" "V3476" "V3477" "V3478" "V3479" "V3480" "V3481" "V3482" "V3483"
## [3484] "V3484" "V3485" "V3486" "V3487" "V3488" "V3489" "V3490" "V3491" "V3492"
## [3493] "V3493" "V3494" "V3495" "V3496" "V3497" "V3498" "V3499" "V3500" "V3501"
## [3502] "V3502" "V3503" "V3504" "V3505" "V3506" "V3507" "V3508" "V3509" "V3510"
## [3511] "V3511" "V3512" "V3513" "V3514" "V3515" "V3516" "V3517" "V3518" "V3519"
## [3520] "V3520" "V3521" "V3522" "V3523" "V3524" "V3525" "V3526" "V3527" "V3528"
## [3529] "V3529" "V3530" "V3531" "V3532" "V3533" "V3534" "V3535" "V3536" "V3537"
## [3538] "V3538" "V3539" "V3540" "V3541" "V3542" "V3543" "V3544" "V3545" "V3546"
## [3547] "V3547" "V3548" "V3549" "V3550" "V3551" "V3552" "V3553" "V3554" "V3555"
## [3556] "V3556" "V3557" "V3558" "V3559" "V3560" "V3561" "V3562" "V3563" "V3564"
## [3565] "V3565" "V3566" "V3567" "V3568" "V3569" "V3570" "V3571" "V3572" "V3573"
## [3574] "V3574" "V3575" "V3576" "V3577" "V3578" "V3579" "V3580" "V3581" "V3582"
## [3583] "V3583" "V3584" "V3585" "V3586" "V3587" "V3588" "V3589" "V3590" "V3591"
## [3592] "V3592" "V3593" "V3594" "V3595" "V3596" "V3597" "V3598" "V3599" "V3600"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200# las filas son el número de puntos de evaluación de la función y las columnas el número de curvas
# Suavizamiento spline
# Creación de la base de funciones
npuntos <- nrow(imagenesEnsxeyMeanFotodf)
base_bspline <- create.bspline.basis(c(1, npuntos), 150)
plot(base_bspline)
summary(base_bspline)##
## Basis object:
##
## Type: bspline
##
## Range: 1 to 200
##
## Number of basis functions: 150# Selección de lambda
gcv <- NULL
lambda_list <- seq(-2, 4, length.out = 30)
# estos son las potencias de 10 de variación del lambda
# inicia con valores muy cercanos a cero 10^-2 hasta 10^4
matrix_data <- as.matrix(imagenesEnsxeyMeanFotodf)
for (lambda in lambda_list) {
lambda <- 10**lambda
param_lambda <- fdPar(base_bspline,
2,
lambda)
fd_smoothEns <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
gcv <- c(gcv, sum(fd_smoothEns$gcv))
}
# Se esta tomando como criterio total la suma de las VC de todas las curvas
plotgcv <-
data.frame(log_lambda = lambda_list,
gcv = gcv) %>% ggplot(aes(x = log_lambda, y = gcv)) +
geom_line(color = "purple", linetype = "dashed") +
theme_light() +
scale_x_continuous(limits = c(0, 4))
plotly::ggplotly(plotgcv)selected_lamdba <- 10**lambda_list[which.min(gcv)]
print(selected_lamdba)## [1] 0.02592944print(log(selected_lamdba, 10))## [1] -1.586207# entrega el valor de lambda donde se minimiza la suma de VC y log de lambda
# Suavizamiento con el lambda elegido
param_lambda <- fdPar(base_bspline,
2,
lambda = selected_lamdba)
fd_smoothEnsMeanFoto <- smooth.basis(argvals = seq(1,
nrow(matrix_data)),
y = matrix_data,
fdParobj = param_lambda)
plot(fd_smoothEnsMeanFoto, xlab= "Pixel", ylab = "Intensidad")
## [1] "done"plot(fd_smoothEnsMeanFoto$fd[1], xlab= "Pixel", ylab = "Intensidad")
## [1] "done"# una curva
# Comparación de curvas originales y suavizadas
fd_fittedEnsMeanFoto <- fitted(fd_smoothEnsMeanFoto)
plot(imagenesEnsxeyMeanFoto[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedEnsMeanFoto[,1],col="2")
# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frágil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))1. Crear el modelo GLM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)
Corre en 2 minutos.
La fracción de acierto global es de 0.3661111, para imágenes dúctiles es de 0.3625, para imágenes frágiles de 0.3216667 y para imágenes de fatiga 0.4141667.
Por fotos: La fracción de acierto global es de 0.333.
Uno de tres dúctiles se adjudican a dúctil. Acierto 33%. Cero de tres frágiles se adjudican correctamente. Acierto 0%. Dos de tres de fatiga se adjudicó correcta. Acierto 66%.
CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(fd_smoothEntMeanFoto$fd,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion
fd_smoothEnsfdata <- fdata(fd_smoothEnsMeanFoto$fd,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo## p1
## CatEns 1 2 3
## 1 439 323 438
## 2 478 388 334
## 3 456 239 505t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion## [1] 0.37t[1,1]/1200## [1] 0.3658333t[2,2]/1200## [1] 0.3233333t[3,3]/1200## [1] 0.4208333Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil## [,1] [,2] [,3]
## [1,] 206 89 105
## [2,] 85 147 168
## [3,] 148 87 165# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil## [,1] [,2] [,3]
## [1,] 152 135 113
## [2,] 173 126 101
## [3,] 153 127 120# Clasificacion por fotos frágiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga## [,1] [,2] [,3]
## [1,] 92 79 229
## [2,] 215 88 97
## [3,] 149 72 179# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frágil y en 3 fatiga
# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenesEntxeyMeanFoto[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")
# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenesEnsxeyMeanFoto[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")